Post-mining of Association Rules: Techniques for Effective Knowledge Extraction

There is often a large number of association rules discovered in data mining practice, making it difficult for users to identify those that are of particular interest to them. Therefore, it is important to remove insignificant rules and prune redundancy as well as summarize, visualize, and post-mine the discovered rules. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction provides a systematic collection of research on the summarization, presentation, and new forms of association rules for post-mining. This book presents researchers, practitioners, and academicians with tools to extract useful and actionable knowledge after discovering a large number of association rules.

[1]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

[2]  Alex Alves Freitas,et al.  On Objective Measures of Rule Surprisingness , 1998, PKDD.

[3]  Marzena Kryszkiewicz,et al.  Representative Association Rules and Minimum Condition Maximum Consequence Association Rules , 1998, PKDD.

[4]  Ke Wang,et al.  Visually Aided Exploration of Interesting Association Rules , 1999, PAKDD.

[5]  Chaomei Chen,et al.  Empirical studies of information visualization: a meta-analysis , 2000, Int. J. Hum. Comput. Stud..

[6]  Toon Calders,et al.  Integrating Pattern Mining in Relational Databases , 2006, PKDD.

[7]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[8]  Geoffrey I. Webb,et al.  K-Optimal Rule Discovery , 2005, Data Mining and Knowledge Discovery.

[9]  K. Pearson.  Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity, and Panmixia , 1896 .

[10]  Hiroyuki Kawano,et al.  Extended Association Algorithm Based on ROC Analysis for Visual Information Navigator , 2002, Progress in Discovery Science.

[11]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[12]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[13]  Xiangji Huang,et al.  Hierarchical grouping of association rules and its application to a real-world domain , 2006, Int. J. Syst. Sci..

[14]  John L. Pfaltz,et al.  Closed Set Mining of Biological Data , 2002, BIOKDD.

[15]  Balaji Padmanabhan,et al.  Knowledge refinement based on the discovery of unexpected patterns in data mining , 2002, Decis. Support Syst..

[16]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[17]  Jean-Daniel Fekete.  The InfoVis Toolkit , 2004 .

[18]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[19]  Rajeev Motwani,et al.  Scalable Techniques for Mining Causal Structures , 1998, Data Mining and Knowledge Discovery.

[20]  Szymon Jaroszewicz,et al.  A General Measure of Rule Interestingness , 2001, PKDD.

[21]  Naresh Jotwani,et al.  Efficient Algorithm for Hierarchical Online Mining of Association Rules , 2006, COMAD.

[22]  James Pustejovsky,et al.  An Application of Lexical Semantics to Knowledge Acquisition from Corpora , 1990, COLING.

[23]  Alexander Tuzhilin,et al.  User-Assisted Knowledge Discovery: How Much Should the User Be Involved , 1996 .

[24]  Régis Gras,et al.  L'implication statistique : nouvelle méthode exploratoire de données : applications à la didactique , 1996 .

[25]  Joydeep Ghosh,et al.  Evaluating the novelty of text-mined rules using lexical knowledge , 2001, KDD '01.

[26]  Douglas H. Fisher,et al.  Bootstrapping rule induction to achieve rule stability and reduction , 2006, Journal of Intelligent Information Systems.

[27]  Christian H. Weiß.  Statistical mining of interesting association rules , 2008 .

[28]  Jennifer Widom,et al.  Clustering association rules , 1997, Proceedings 13th International Conference on Data Engineering.

[29]  Michal Mikolajczyk.  Reducing Number of Decision Rules by Joining , 2002, Rough Sets and Current Trends in Computing.

[30]  Ryszard S. Michalski,et al.  Pattern Recognition as Rule-Guided Inductive Inference , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Le Gruenwald,et al.  Estimating Missing Values in Related Sensor Data Streams , 2005, COMAD.

[32]  Christophe Rigotti,et al.  A condensed representation to find frequent patterns , 2001, PODS '01.

[33]  Osmar R. Zaïane,et al.  Considering Re-occurring Features in Associative Classifiers , 2005, PAKDD.

[34]  Jia-Ling Koh,et al.  An Efficient Approach for Maintaining Association Rules Based on Adjusting FP-Tree Structures1 , 2004, DASFAA.

[35]  George Boole,et al.  The mathematical analysis of logic , 1948 .

[36]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[37]  J. Downing,et al.  Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.

[38]  E. Lehmann.  Testing Statistical Hypotheses , 1960 .

[39]  Johannes Gehrke,et al.  A framework for measuring changes in data characteristics , 1999, PODS '99.

[40]  Sanjay Chawla,et al.  On local pruning of association rules using directed hypergraphs , 2004, Proceedings. 20th International Conference on Data Engineering.

[41]  Christian Borgelt,et al.  EFFICIENT IMPLEMENTATIONS OF APRIORI AND ECLAT , 2003 .

[42]  J. A. Hartigan,et al.  Mosaics for Contingency Tables , 1981 .

[43]  Howard J. Hamilton,et al.  Knowledge discovery and measures of interest , 2001 .

[44]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[45]  Keith Shwayder.  Combining decision rules in a decision table , 1975, CACM.

[46]  Pang-Ning Tan,et al.  Interestingness Measures for Association Patterns : A Perspective , 2000, KDD 2000.

[47]  François Jacquenet,et al.  Efficient Management of Non Redundant Rules in Large Pattern Bases: A Bitmap Approach , 2006, ICEIS.

[48]  Peter A. Flach,et al.  Rule Evaluation Measures: A Unifying View , 1999, ILP.

[49]  Zhan Li,et al.  Knowledge and Information Systems , 2007 .

[50]  Frans Coenen,et al.  An evaluation of approaches to classification rule selection , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[51]  Galit Shmueli,et al.  Statistical issues and challenges associated with rapid detection of bio‐terrorist attacks , 2005, Statistics in medicine.

[52]  Sanjay Chawla,et al.  CCCS: a top-down associative classifier for imbalanced class distribution , 2006, KDD '06.

[53]  Divyakant Agrawal,et al.  Efficient Computation of Frequent and Top-k Elements in Data Streams , 2005, ICDT.

[54]  Szymon Jaroszewicz,et al.  Interestingness of frequent itemsets using Bayesian networks as background knowledge , 2004, KDD.

[55]  Bruno Crémilleux,et al.  A Unified View of Objective Interestingness Measures , 2007, MLDM.

[56]  B. Shekar,et al.  A Framework for Evaluating Knowledge-Based Interestingness of Association Rules , 2004, Fuzzy Optim. Decis. Mak..

[57]  Yiyu Yao,et al.  Peculiarity Oriented Multidatabase Mining , 2003, IEEE Trans. Knowl. Data Eng..

[58]  Ming-Syan Chen,et al.  On the mining of substitution rules for statistically dependent items , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[59]  Philip S. Yu,et al.  Moment: maintaining closed frequent itemsets over a stream sliding window , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[60]  H. Jeffreys.  Some Tests of Significance, Treated by the Theory of Probability , 1935, Mathematical Proceedings of the Cambridge Philosophical Society.

[61]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[62]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[63]  Wynne Hsu,et al.  Finding Interesting Patterns Using User Expectations , 1999, IEEE Trans. Knowl. Data Eng..

[64]  Yue Xu,et al.  Generating concise association rules , 2007, CIKM '07.

[65]  Mohammed J. Zaki,et al.  Efficiently mining maximal frequent itemsets , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[66]  Girish Keshav Palshikar,et al.  Association Rules Mining Using Heavy Itemsets , 2005, COMAD.

[67]  Giuseppe Psaila,et al.  Active Data Mining , 1995, Encyclopedia of GIS.

[68]  Bin Tang,et al.  Applying Association Rules for Interesting Recommendations Using Rule Templates , 2004, PAKDD.

[69]  Gregory Piatetsky-Shapiro,et al.  The interestingness of deviations , 1994 .

[70]  Dimitrios Gunopulos,et al.  Constraint-Based Rule Mining in Large, Dense Databases , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[71]  Kate Smith-Miles,et al.  Redundant association rules reduction techniques , 2007, Int. J. Bus. Intell. Data Min..

[72]  Mohammed J. Zaki.  Mining Non-Redundant Association Rules , 2004, Data Min. Knowl. Discov..

[73]  James Bailey,et al.  Mining minimal distinguishing subsequence patterns with gap constraints , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[74]  Alex Alves Freitas,et al.  On rule interestingness measures , 1999, Knowl. Based Syst..

[75]  Roberto J. Bayardo.  Brute-Force Mining of High-Confidence Classification Rules , 1997, KDD.

[76]  Philip S. Yu,et al.  Online generation of association rules , 1998, Proceedings 14th International Conference on Data Engineering.

[77]  Amitava Datta,et al.  VisAR : A New Technique for Visualizing Mined Association Rules , 2005, ADMA.

[78]  Robert C. Holte,et al.  Cost curves: An improved method for visualizing classifier performance , 2006, Machine Learning.

[79]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[80]  Erik D. Demaine,et al.  Frequency Estimation of Internet Packet Streams with Limited Space , 2002, ESA.

[81]  Geert Wets,et al.  Using association rules for product assortment decisions: a case study , 1999, KDD '99.

[82]  Engelbert Mephu Nguifo,et al.  Frequent closed itemset based algorithms: a thorough structural and analytical survey , 2006, SKDD.

[83]  Jian Pei,et al.  CLOSET+: searching for the best strategies for mining frequent closed itemsets , 2003, KDD '03.

[84]  Sharma Chakravarthy,et al.  Visualization of association rules over relational DBMSs , 2003, SAC '03.

[85]  José Rouillard,et al.  An Interactive Approach to Display Large Sets of Association Rules , 2007, HCI.

[86]  Howard J. Hamilton,et al.  Choosing the Right Lens: Finding What is Interesting in Data Mining , 2007, Quality Measures in Data Mining.

[87]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[88]  Srinivasan Parthasarathy,et al.  Mining Frequent Itemsets in Evolving Databases , 2002, SDM.

[89]  Li Yang,et al.  Visualizing Frequent Itemsets, Association Rules, and Sequential Patterns in Parallel Coordinates , 2003, ICCSA.

[90]  Christian Borgelt,et al.  Induction of Association Rules: Apriori Implementation , 2002, COMPSTAT.

[91]  Hans-Peter Kriegel,et al.  Visualization Techniques for Mining Large Databases: A Comparison , 1996, IEEE Trans. Knowl. Data Eng..

[92]  Dan A. Simovici,et al.  Generating an informative cover for association rules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[93]  Jinyan Li,et al.  A new concise representation of frequent itemsets using generators and a positive border , 2008, Knowledge and Information Systems.

[94]  Ayaho Miyamoto,et al.  Extraction of minimal decision algorithm using rough sets and genetic algorithm , 2007, Systems and Computers in Japan.

[95]  J. Hackman,et al.  Development of the Job Diagnostic Survey , 1975 .

[96]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[97]  Solange Oliveira Rezende,et al.  Combining Quality Measures to Identify Interesting Association Rules , 2004, IBERAMIA.

[98]  Fabrice Guillet,et al.  Quality Measures in Data Mining (Studies in Computational Intelligence) , 2007 .

[99]  Wojciech Ziarko,et al.  Acquisition of hierarchy‐structured probabilistic decision tables and rules from data , 2003, Expert Syst. J. Knowl. Eng..

[100]  Fabrice Guillet,et al.  A user-driven and quality-oriented visualization for mining association rules , 2003, Third IEEE International Conference on Data Mining.

[101]  Francesco Bonchi,et al.  On condensed representations of constrained frequent patterns , 2005, Knowledge and Information Systems.

[102]  Roberto J. Bayardo,et al.  Mining the most interesting rules , 1999, KDD '99.

[103]  Laks V. S. Lakshmanan,et al.  Mining frequent itemsets with convertible constraints , 2001, Proceedings 17th International Conference on Data Engineering.

[104]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[105]  Balaji Padmanabhan,et al.  Small is beautiful: discovering the minimal set of unexpected patterns , 2000, KDD '00.

[106]  Yonatan Aumann,et al.  Borders: An Efficient Algorithm for Association Generation in Dynamic Databases , 1999, Journal of Intelligent Information Systems.

[107]  A. J. Feelders,et al.  MAMBO: Discovering Association Rules Based on Conditional Independencies , 2001, IDA.

[108]  Régis Gras,et al.  Assessing rule interestingness with a probabilistic measure of deviation from equilibrium , 2005 .

[109]  D. Cheung,et al.  Maintenance of Discovered Association Rules , 2002 .

[110]  D. Cox,et al.  SOME QUICK SIGN TESTS FOR TREND IN LOCATION AND DISPERSION , 1955 .

[111]  Lisa Singh,et al.  Visual Mining of Multi-Modal Social Networks at Different Abstraction Levels , 2007, 2007 11th International Conference Information Visualization (IV '07).

[112]  Heikki Mannila,et al.  Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.

[113]  Xiaohua Hu,et al.  Learning maximal generalized decision rules via discretization, generalization and rough set feature selection , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[114]  Régis Gras,et al.  An overview of the Statistical Implicative development , 2008 .

[115]  Nicolas Pasquier,et al.  Mining association rules using frequent closed itemsets , 2005 .

[116]  Helena Chmura Kraemer,et al.  Evaluating Medical Tests: Objective and Quantitative Guidelines , 1992 .

[117]  Miao Duo-qian,et al.  Information-based algorithm for reduction of knowledge , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[118]  Philip K. Chan,et al.  Systems for Knowledge Discovery in Databases , 1993, IEEE Trans. Knowl. Data Eng..

[119]  Mohammed J. Zaki,et al.  MIRAGE : A Framework for Mining , Exploring and Visualizing Minimal Association Rules ∗ , 2003 .

[120]  Amedeo Napoli,et al.  Towards a text mining methodology using association rule extraction , 2006, Soft Comput..

[121]  Laks V. S. Lakshmanan,et al.  Exploiting succinct constraints using FP-trees , 2002, SKDD.

[122]  Jinyan Li,et al.  Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness , 1998, PAKDD.

[123]  P. F. Russell,et al.  On Habitat and Association of Species of Anopheline Larvae in South-eastern Madras. , 1940 .

[124]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[125]  Elena Baralis,et al.  Designing Templates for Mining Association Rules , 2004, Journal of Intelligent Information Systems.

[126]  George Boole,et al.  An Investigation of the Laws of Thought: Frontmatter , 2009 .

[127]  Patrick Meyer,et al.  On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid , 2008, Eur. J. Oper. Res..

[128]  Zdzislaw Pawlak,et al.  Rough Sets and Decision Algorithms , 2000, Rough Sets and Current Trends in Computing.

[129]  Michael Luxenburger,et al.  Implications partielles dans un contexte , 1991 .

[130]  Balaji Padmanabhan,et al.  A Belief-Driven Method for Discovering Unexpected Patterns , 1998, KDD.

[131]  Geoffrey I. Webb.  Efficient search for association rules , 2000, KDD '00.

[132]  Thomas Sudkamp,et al.  Examples, counterexamples, and measuring fuzzy associations , 2005, Fuzzy Sets Syst..

[133]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Itemset Mining , 2002, SDM.

[134]  Laks V. S. Lakshmanan,et al.  Exploratory mining via constrained frequent set queries , 1999, SIGMOD '99.

[135]  Takahira Yamaguchi,et al.  Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis , 2004, PKDD.

[136]  Xindong Wu,et al.  Mining maximal frequent itemsets from data streams , 2007, J. Inf. Sci..

[137]  Giuseppe Psaila,et al.  A New SQL-like Operator for Mining Association Rules , 1996, VLDB.

[138]  Sigal Sahar,et al.  Interestingness via what is not interesting , 1999, KDD '99.

[139]  Deborah R. Carvalho,et al.  Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest , 2005, PKDD.

[140]  Jiawei Han,et al.  MAIDS: mining alarming incidents from data streams , 2004, SIGMOD '04.

[141]  Jian Pei,et al.  Minimum Description Length Principle: Generators Are Preferable to Closed Patterns , 2006, AAAI.

[142]  Marcos Aurélio Domingues,et al.  Using Taxonomies to Facilitate the Analysis of the Association Rules , 2011, ArXiv.

[143]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[144]  Jaideep Srivastava,et al.  Selecting the right objective measure for association analysis , 2004, Inf. Syst..

[145]  Engelbert Mephu Nguifo,et al.  A new Informative Generic Base of Association Rules , 2005, CLA.

[146]  Wynne Hsu,et al.  Multi-level organization and summarization of the discovered rules , 2000, KDD '00.

[147]  Catherine Plaisant,et al.  The challenge of information visualization evaluation , 2004, AVI.

[148]  Donato Malerba,et al.  A Comparative Analysis of Methods for Pruning Decision Trees , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[149]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[150]  Jan M. Zytkow,et al.  From Contingency Tables to Various Forms of Knowledge in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[151]  Szymon Jaroszewicz,et al.  Pruning Redundant Association Rules Using Maximum Entropy Principle , 2002, PAKDD.

[152]  Jacob Shapiro,et al.  Algorithm for automatic construction of query formulations in Boolean form , 1991, J. Am. Soc. Inf. Sci..

[153]  Osmar R. Zaïane,et al.  Text document categorization by term association , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[154]  Helena C. Kraemer,et al.  Assessment of 2 × 2 Associations: Generalization of Signal-Detection Methodology , 1988 .

[155]  Alfred Inselberg,et al.  Parallel coordinates: a tool for visualizing multi-dimensional geometry , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.

[156]  Osmar R. Zaïane,et al.  Fast parallel association rule mining without candidacy generation , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[157]  Philip S. Yu,et al.  Mining Associations with the Collective Strength Approach , 2001, IEEE Trans. Knowl. Data Eng..

[158]  Heikki Mannila,et al.  Pruning and grouping of discovered association rules , 1995 .

[159]  Ee-Peng Lim,et al.  Mining Multi-Level Rules with Recurrent Items using FP'-Tree , 2001 .

[160]  Fabrice Guillet,et al.  A 2D-3D visualization support for human-centered rule mining , 2007, Comput. Graph..

[161]  Boris Rachev,et al.  Multidimensional models: constructing data CUBE , 2004, CompSysTech '04.

[162]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[163]  Ping Chen,et al.  Semantic Analysis of Association Rules , 2008, FLAIRS Conference.

[164]  Huang-Cheng Kuo,et al.  Building a Concept Hierarchy from a Distance Matrix , 2005, Intelligent Information Systems.

[165]  Carlo Zaniolo,et al.  Designing an inductive data stream management system: the stream mill experience , 2008, SSPS '08.

[166]  Jinyan Li,et al.  CAEP: Classification by Aggregating Emerging Patterns , 1999, Discovery Science.

[167]  Soon Myoung Chung,et al.  Parallel mining of association rules from text databases , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[168]  Jiuyong Li.  On optimal rule discovery , 2006 .

[169]  Ee-Peng Lim,et al.  CrystalClear: Active visualization of association rules , 2002 .

[170]  Laks V. S. Lakshmanan,et al.  Mining unexpected rules by pushing user dynamics , 2003, KDD '03.

[171]  Camila Delefrate Martins.  Construção semi-automática de taxonomias para generalização de regras de associação , 2006 .

[172]  Frederick Mosteller,et al.  Association and Estimation in Contingency Tables , 1968 .

[173]  Padhraic Smyth,et al.  An Information Theoretic Approach to Rule Induction from Databases , 1992, IEEE Trans. Knowl. Data Eng..

[174]  Yves Bastide,et al.  Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis , 2001, KI/ÖGAI.

[175]  Chen Zheng,et al.  A Distance-Based Approach to Find Interesting Patterns , 2003, DaWaK.

[176]  Philip Yu,et al.  WAR: Weighted association rules for item intensities , 2007, Knowledge and Information Systems.

[177]  Marzena Kryszkiewicz.  Generalized disjunction-free representation of frequent patterns with negation , 2005, J. Exp. Theor. Artif. Intell..

[178]  David B. Fogel,et al.  The Advantages of Evolutionary Computation , 1997, BCEC.

[179]  Peter A. Flach,et al.  A Response to Webb and Ting’s On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions , 2005, Machine Learning.

[180]  R. Kruse,et al.  Relevance Feedback for Association Rules using Fuzzy Score Aggregation , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[181]  Hanhong Xue,et al.  Rule Generalization by Condition Combination , 1998, PAKDD.

[182]  Philip S. Yu,et al.  Efficient parallel data mining for association rules , 1995, CIKM '95.

[183]  Chris North,et al.  Snap-together visualization: can users construct and operate coordinated visualizations? , 2000, Int. J. Hum. Comput. Stud..

[184]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[185]  Fabrice Guillet,et al.  A User-Driven Process for Mining Association Rules , 2000, PKDD.

[186]  Ada Wai-Chee Fu,et al.  Mining frequent itemsets without support threshold: with and without item constraints , 2004, IEEE Transactions on Knowledge and Data Engineering.

[187]  Jianyong Wang,et al.  Mining sequential patterns by pattern-growth: the PrefixSpan approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[188]  Kotagiri Ramamohanarao,et al.  Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets , 2000, KDD '00.

[189]  Jiawei Han,et al.  Mining recurrent items in multimedia with progressive resolution refinement , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[190]  Carlo Zaniolo,et al.  A data stream language and system designed for power and extensibility , 2006, CIKM '06.

[191]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[192]  Gerd Stumme,et al.  Mining frequent patterns with counting inference , 2000, SKDD.

[193]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[194]  Bart Goethals,et al.  Mining Non-Derivable Association Rules , 2005, SDM.

[195]  Osmar R. Zaïane,et al.  Mining Positive and Negative Association Rules: An Approach for Confined Rules , 2004, PKDD.

[196]  Elena Baralis,et al.  A lazy approach to pruning classification rules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[197]  Gregory Piatetsky-Shapiro,et al.  Summary from the KDD-03 panel: data mining: the next 10 years , 2003, SKDD.

[198]  Xindong Wu,et al.  Mining Both Positive and Negative Association Rules , 2002, ICML.

[199]  David Wai-Lok Cheung,et al.  A General Incremental Technique for Maintaining Discovered Association Rules , 1997, DASFAA.

[200]  Peter A. Flach,et al.  ROCCER: An Algorithm for Rule Learning Based on ROC Analysis , 2005, IJCAI.

[201]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[202]  Francesco Bonchi,et al.  On closed constrained frequent pattern mining , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[203]  Luigi Palopoli,et al.  On the Complexity of Mining Association Rules , 2001, SEBD.

[204]  Sunita Sarawagi,et al.  Mining Surprising Patterns Using Temporal Description Length , 1998, VLDB.

[205]  J. Michael Dunn,et al.  Relevance Logic and Entailment , 1986 .

[206]  Hongjun Lu,et al.  Efficient Search of Reliable Exceptions , 1999, PAKDD.

[207]  Jinyan Li,et al.  Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed , 2007, PAKDD.

[208]  Nick Cercone,et al.  RuleViz: a model for visualizing knowledge discovery process , 2000, KDD '00.

[209]  Keith C. C. Chan,et al.  Mining changes in association rules: a fuzzy approach , 2005, Fuzzy Sets Syst..

[210]  Jean-François Boulicaut,et al.  Iterative Bayesian Network Implementation by Using Annotated Association Rules , 2006, EKAW.

[211]  Carson Kai-Sang Leung,et al.  CanTree: a tree structure for efficient incremental mining of frequent patterns , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[212]  A. D. Morgan.  Formal Logic: Or, The Calculus of Inference, Necessary and Probable , 2002 .

[213]  Manpreet Singh,et al.  Conceptual Multidimensional Model , 2007 .

[214]  Gavriel Salvendy,et al.  Design and evaluation of visualization support to facilitate association rules modeling , 2006, Int. J. Hum. Comput. Interact..

[215]  Geert Wets,et al.  Reducing redundancy in characteristic rule discovery by using integer programming techniques , 2000, Intell. Data Anal..

[216]  M.A. Domingues,et al.  Post-processing of Association Rules using Taxonomies , 2005, 2005 portuguese conference on artificial intelligence.

[217]  Yiming Ma,et al.  Improving an Association Rule Based Classifier , 2000, PKDD.

[218]  Dino Pedreschi,et al.  Efficient breadth-first mining of frequent pattern with monotone constraints , 2005, Knowledge and Information Systems.

[219]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[220]  P. McNicholas.  Association rule analysis of CAO data , 2007 .

[221]  Reda Alhajj,et al.  Visual interface for online watching of frequent itemset generation in Apriori and Eclat , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

[222]  D J Rogers,et al.  A Computer Program for Classifying Plants. , 1960, Science.

[223]  Gerard Salton,et al.  The SMART Information Retrieval System after 30 years - Panel. , 1991, SIGIR 1991.

[224]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[225]  Carlo Zaniolo,et al.  Optimization of sequence queries in database systems , 2001, PODS '01.

[226]  Arne Koopman,et al.  Frequent Patterns that Compress , 2006 .

[227]  William Stanley Jevons,et al.  Pure Logic and Other Minor Works , 1991 .

[228]  Amihai Motro.  Extending the Relational Database Model to Support Goal Queries , 1986, Expert Database Conf..

[229]  Solange Oliveira Rezende,et al.  Obtaining and Evaluating Generalized Association Rules , 2007, ICEIS.

[230]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[231]  Ming-Syan Chen,et al.  Sliding window filtering: an efficient method for incremental mining on a time-variant database , 2005, Inf. Syst..

[232]  Maria Francesca Costabile,et al.  Visualizing Association Rules in a Framework for Visual Data Mining , 2005, From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments.

[233]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[234]  Jean-François Boulicaut,et al.  Optimization of association rule mining queries , 2002, Intell. Data Anal..

[235]  Roberto J. Bayardo,et al.  Efficiently mining long patterns from databases , 1998, SIGMOD '98.

[236]  Laks V. S. Lakshmanan,et al.  Optimization of constrained frequent set queries with 2-variable constraints , 1999, SIGMOD '99.

[237]  Tom Fawcett.  PRIE: a system for generating rulelists to maximize ROC performance , 2008, Data Mining and Knowledge Discovery.

[238]  Dario Bruzzese,et al.  Combining visual techniques for Association Rules exploration , 2004, AVI.

[239]  G Salvendy,et al.  Vizualization to facilitate association rules modeling: A review , 2005 .

[240]  Yves Kodratoff,et al.  Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts , 2001, Machine Learning and Its Applications.

[241]  Jitender S. Deogun,et al.  SARM - Succinct Association Rule Mining: An Approach to Enhance Association Mining , 2005, ISMIS.

[242]  Hiroki Arimura,et al.  LCM: An Efficient Algorithm for Enumerating Frequent Closed Item Sets , 2003, FIMI.

[243]  Jean-François Boulicaut,et al.  Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries , 2004, Data Mining and Knowledge Discovery.

[244]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[245]  Ivan Bruha,et al.  Postprocessing in machine learning and data mining , 2000, SKDD.

[246]  Engelbert Mephu Nguifo,et al.  Succinct System of Minimal Generators: A Thorough Study, Limitations and New Definitions , 2006, CLA.

[247]  Maria Cristina Ferreira de Oliveira,et al.  An Itemset-Driven Cluster-Oriented Approach to Extract Compact and Meaningful Sets of Association Rules , 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

[248]  Tzung-Pei Hong,et al.  Learning cross-level certain and possible rules by rough sets , 2008, Expert Syst. Appl..

[249]  Patrick Meyer,et al.  Association Rule Interestingness Measures: Experimental and Theoretical Studies , 2007, Quality Measures in Data Mining.

[250]  Francesca A. Lisi,et al.  Supporting Data Analysis Through Visualizations , 2001 .

[251]  G. Yule.  NOTES ON THE THEORY OF ASSOCIATION OF ATTRIBUTES IN STATISTICS , 1903 .

[252]  Wynne Hsu,et al.  Discovering the set of fundamental rule changes , 2001, KDD '01.

[253]  Shamkant B. Navathe,et al.  Mining for strong negative associations in a large database of customer transactions , 1998, Proceedings 14th International Conference on Data Engineering.

[254]  Nicolas Pasquier,et al.  Pruning closed itemset lattices for associations rules , 1998, BDA.

[255]  Wynne Hsu,et al.  Mining Changes for Real-Life Applications , 2000, DaWaK.

[256]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[257]  José M. Peña,et al.  Minimal decision rules based on the apriori algorithm , 2001 .

[258]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[259]  Osmar R. Zaïane,et al.  An associative classifier based on positive and negative rules , 2004, DMKD '04.

[260]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[261]  Ke Wang,et al.  Growing decision trees on support-less association rules , 2000, KDD '00.

[262]  Shichao Zhang,et al.  Information enhancement for data mining , 2011, WIREs Data Mining Knowl. Discov..

[263]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[264]  Régis Gras,et al.  Implication Intensity: From the Basic Statistical Definition to the Entropic Version , 2003 .

[265]  Jianyong Wang,et al.  HARMONY: Efficiently Mining the Best Rules for Classification , 2005, SDM.

[266]  Rosa Meo.  Theory of dependence values , 2000, TODS.

[267]  Haim Levkowitz,et al.  From Visual Data Exploration to Visual Data Mining: A Survey , 2003, IEEE Trans. Vis. Comput. Graph..

[268]  Detlef D. Nauck,et al.  Detecting temporally redundant association rules , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

[269]  Gösta Grahne,et al.  Efficiently Using Prefix-trees in Mining Frequent Itemsets , 2003, FIMI.

[270]  Gerd Stumme,et al.  Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets , 2000, Computational Logic.

[271]  Raz Tamir,et al.  On a confidence gain measure for association rule discovery and scoring , 2004, The VLDB Journal.

[272]  Yiming Ma,et al.  Analyzing the interestingness of association rules from the temporal dimension , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[273]  Gerd Stumme,et al.  Formal Concept Analysis: foundations and applications , 2005 .

[274]  Gerd Stumme,et al.  Generating a Condensed Representation for Association Rules , 2005, Journal of Intelligent Information Systems.

[275]  D. Haussler,et al.  Boolean Feature Discovery in Empirical Learning , 1990, Machine Learning.

[276]  Mohammed J. Zaki,et al.  Efficient algorithms for mining closed itemsets and their lattice structure , 2005, IEEE Transactions on Knowledge and Data Engineering.

[277]  Kim J. Vicente,et al.  An experimental evaluation of transparent menu usage , 1996, CHI.

[278]  Hongjun Lu,et al.  Relative Measure for Mining Interesting Rules , 2000 .

[279]  Kenneth McGarry,et al.  A survey of interestingness measures for knowledge discovery , 2005, The Knowledge Engineering Review.

[280]  Jaideep Srivastava,et al.  Selecting the right interestingness measure for association patterns , 2002, KDD.

[281]  George W. Furnas,et al.  A fisheye follow-up: further reflections on focus + context , 2006, CHI.

[282]  Szymon Jaroszewicz,et al.  Fast discovery of unexpected patterns in data, relative to a Bayesian network , 2005, KDD '05.

[283]  Peter A. Flach.  The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics , 2003, ICML.

[284]  Peter I. Cowling,et al.  MMAC: a new multi-class, multi-label associative classification approach , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[285]  Howard J. Hamilton,et al.  Evaluation of Interestingness Measures for Ranking Discovered Knowledge , 2001, PAKDD.

[286]  Mu-Chen Chen,et al.  Mining changes in customer behavior in retail marketing , 2005, Expert Syst. Appl..

[287]  Ramana Rao,et al.  A focus+context technique based on hyperbolic geometry for visualizing large hierarchies , 1995, CHI '95.

[288]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[289]  Rosane Minghim,et al.  Visual Mapping of Text Collections through a Fast High Precision Projection Technique , 2006, Tenth International Conference on Information Visualisation (IV'06).

[290]  Philip S. Yu,et al.  Scoring the Data Using Association Rules , 2003, Applied Intelligence.

[291]  Carlo Zaniolo,et al.  ATLaS: A Native Extension of SQL for Data Mining , 2003, SDM.

[292]  David Maier,et al.  The Theory of Relational Databases , 1983 .

[293]  G. Yule,et al.  On the association of attributes in statistics, with examples from the material of the childhood society, &c , 1900, Proceedings of the Royal Society of London.

[294]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[295]  A. Ochiai.  Zoogeographical Studies on the Soleoid Fishes Found in Japan and its Neighbouring Regions-III , 1957 .

[296]  Jian Pei,et al.  Constrained frequent pattern mining: a pattern-growth view , 2002, SKDD.

[297]  Jock D. Mackinlay,et al.  The perspective wall: detail and context smoothly integrated , 1991, CHI.

[298]  Johannes Gehrke,et al.  MAFIA: a maximal frequent itemset algorithm for transactional databases , 2001, Proceedings 17th International Conference on Data Engineering.

[299]  Rokia Missaoui,et al.  Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges , 2004, ICFCA.

[300]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[301]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[302]  Peter Clark,et al.  Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.

[303]  Wilfred Ng,et al.  Effective elimination of redundant association rules , 2007, Data Mining and Knowledge Discovery.

[304]  JOHANNES FÜRNKRANZ,et al.  Separate-and-Conquer Rule Learning , 1999, Artificial Intelligence Review.

[305]  Marzena Kryszkiewicz.  Concise Representations of Association Rules , 2002, Pattern Detection and Discovery.

[306]  J. G. Ganascia,et al.  Deriving the learning bias from rule properties , 1991 .

[307]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[308]  Randy Goebel,et al.  Visual Data Mining of Web Navigational Data , 2007, 2007 11th International Conference Information Visualization (IV '07).

[309]  Frédéric Cuppens,et al.  How to recognize interesting topics to provide cooperative answering , 1989, Inf. Syst..

[310]  Rajeev Motwani,et al.  Beyond Market Baskets: Generalizing Association Rules to Dependence Rules , 1998, Data Mining and Knowledge Discovery.

[311]  Osmar R. Zaïane,et al.  On Pruning and Tuning Rules for Associative Classifiers , 2005, KES.

[312]  Dennis McLeod,et al.  Ontology-driven Rule Generalization and Categorization for Market Data , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[313]  Wynne Hsu,et al.  Using General Impressions to Analyze Discovered Classification Rules , 1997, KDD.

[314]  Jiawei Han,et al.  CPAR: Classification based on Predictive Association Rules , 2003, SDM.

[315]  Xiaohua Hu,et al.  Discovering Maximal Generalized Decision Rules Through Horizontal and Vertical Data Reduction , 2001, Comput. Intell..

[316]  Fadi A. Thabtah,et al.  A review of associative classification mining , 2007, The Knowledge Engineering Review.

[317]  Elena Baralis,et al.  A Lazy Approach to Associative Classification , 2008, IEEE Transactions on Knowledge and Data Engineering.

[318]  Li Meng,et al.  Rough Set Theory and Its Application , 2001 .

[319]  Edward Omiecinski,et al.  Alternative Interest Measures for Mining Associations in Databases , 2003, IEEE Trans. Knowl. Data Eng..

[320]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[321]  Chris North,et al.  Snap-Together Visualization: A User Interface for Coordinating Visualizations via Relational Schemata , 2003 .

[322]  Gregory Piatetsky-Shapiro,et al.  Knowledge discovery in databases: 10 years after , 2000, SKDD.

[323]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[324]  Solange Oliveira Rezende,et al.  An Analytical Evaluation of Objective Measures Behavior for Generalized Association Rules , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[325]  Sadok Ben Yahia,et al.  Prince: An Algorithm for Generating Rule Bases Without Closure Computations , 2005, DaWaK.

[326]  J. Loevinger.  A systematic approach to the construction and evaluation of tests of ability. , 1947 .

[327]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[328]  Jean-Marc Adamo,et al.  Data Mining for Association Rules and Sequential Patterns , 2000, Springer New York.

[329]  Chris Chatfield,et al.  The Analysis of Time Series : An Introduction, Sixth Edition , 2003 .

[330]  Engelbert Mephu Nguifo,et al.  A scalable association rule visualization towards displaying large amounts of knowledge , 2007, 2007 11th International Conference Information Visualization (IV '07).

[331]  Régis Gras,et al.  Using information-theoretic measures to assess association rule interestingness , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[332]  Heikki Mannila,et al.  Association Rule Selection in a Data Mining Environment , 1999, PKDD.

[333]  Robert R. Sokal,et al.  A statistical method for evaluating systematic relationships , 1958 .

[334]  Jiawei Han,et al.  Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes , 1997, KDD.

[335]  Fabrice Guillet,et al.  Interactive visual exploration of association rules with rule-focusing methodology , 2007, Knowledge and Information Systems.

[336]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[337]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[338]  Dario Bruzzese,et al.  Statistical Pruning of Discovered Association Rules , 2001, Comput. Stat..

[339]  S. Yahia,et al.  RV IS : ” scalable ” visualization of generic association rules , 2007 .

[340]  Nicolas Pasquier,et al.  Efficient Mining of Association Rules Using Closed Itemset Lattices , 1999, Inf. Syst..

[341]  Hava T. Siegelmann,et al.  Active Information Retrieval , 2001, NIPS.

[342]  Miao Duo,et al.  A HEURISTIC ALGORITHM FOR REDUCTION OF KNOWLEDGE , 1999 .

[343]  Jean-François Boulicaut,et al.  A Survey on Condensed Representations for Frequent Sets , 2004, Constraint-Based Mining and Inductive Databases.

[344]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.

[345]  Jinyan Li,et al.  Relative risk and odds ratio: a data mining perspective , 2005, PODS '05.

[346]  Alípio Mário Jorge,et al.  Post-processing environment for browsing large sets of association rules , 2002 .

[347]  Xuemin Lin,et al.  Term Graph Model for Text Classification , 2005, ADMA.

[348]  Toon Calders,et al.  Non-derivable itemset mining , 2007, Data Mining and Knowledge Discovery.

[349]  Maria Cristina Ferreira de Oliveira,et al.  Including the user in the knowledge discovery loop: interactive itemset-driven rule extraction , 2008, SAC '08.

[350]  Silvia Miksch,et al.  Applying information visualization techniques to capture and explore the course of cognitive behavioral therapy , 2003, SAC '03.

[351]  Howard J. Hamilton,et al.  Interestingness measures for data mining: A survey , 2006, CSUR.

[352]  Detlef D. Nauck,et al.  A Framework for Discovering and Analyzing Changing Customer Segments , 2007, ICDM.

[353]  Philip S. Yu,et al.  Mining Large Itemsets for Association Rules , 1998, IEEE Data Eng. Bull..

[354]  Ronald R. Yager,et al.  On the inclusion of importances in OWA aggregations , 1997 .

[355]  Geert Wets,et al.  Defining interestingness for association rules , 2003 .

[356]  Marcin Michalak,et al.  NetTRS - Induction and Postprocessing of Decision Rules , 2006, RSCTC.

[357]  Balaji Padmanabhan,et al.  Unexpectedness as a Measure of Interestingness in Knowledge Discovery , 1999, Decis. Support Syst..

[358]  Julien Blanchard.  Un système de visualisation pour l'extraction, l'évaluation, et l'exploration interactives des règles d'association , 2005 .

[359]  Pak Chung Wong,et al.  Visualizing association rules for text mining , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[360]  Ayhan Demiriz,et al.  A Framework for Visualizing Association Mining Results , 2006, ISCIS.

[361]  Ioannis Kopanakis,et al.  Visual data mining modeling techniques for the visualization of mining outcomes , 2003, J. Vis. Lang. Comput..

[362]  Elena Baralis,et al.  Essential classification rule sets , 2004, TODS.

[363]  Heike Hofmann,et al.  Visualizing association rules with interactive mosaic plots , 2000, KDD '00.

[364]  Hans-Peter Kriegel,et al.  Incremental Clustering for Mining in a Data Warehousing Environment , 1998, VLDB.

[365]  Yijun Lu,et al.  Concept Hierarchy in Data Mining: Specificat ion, Generat ion and Implement at ion , 1997 .

[366]  Rudolf Kruse,et al.  Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval , 2007, SGAI Conf..

[367]  Zhigang Li,et al.  Efficient data mining for maximal frequent subtrees , 2003, Third IEEE International Conference on Data Mining.

[368]  Howard J. Hamilton,et al.  Heuristic Measures of Interestingness , 1999, PKDD.

[369]  Engelbert Mephu Nguifo,et al.  Generic Association Rule Bases: Are They so Succinct? , 2006, CLA.

[370]  Marzena Kryszkiewicz.  Concise representation of frequent patterns based on disjunction-free generators , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[371]  Osmar R. Zaïane,et al.  Incremental mining of frequent patterns without candidate generation or support constraint , 2003, Seventh International Database Engineering and Applications Symposium, 2003. Proceedings..

[372]  Hongjun Lu,et al.  Exception Rule Mining with a Relative Interestingness Measure , 2000, PAKDD.

[373]  B. Shekar,et al.  A relatedness-based data-driven approach to determination of interestingness of association rules , 2005, SAC '05.

[374]  Qiang Yang,et al.  Mining high utility itemsets , 2003, Third IEEE International Conference on Data Mining.

[375]  Mohammed J. Zaki.  Generating non-redundant association rules , 2000, KDD '00.

[376]  Ron Kohavi,et al.  The Case against Accuracy Estimation for Comparing Induction Algorithms , 1998, ICML.

[377]  ZhaoHui Tang,et al.  Building data mining solutions with OLE DB for DM and XML for analysis , 2005, SGMD.

[378]  Nan Jiang,et al.  CFI-Stream: mining closed frequent itemsets in data streams , 2006, KDD '06.

[379]  W. J. Langford.  Statistical Methods , 1959, Nature.

[380]  Myra Spiliopoulou,et al.  Efficient Monitoring of Patterns in Data Mining Environments , 2003, ADBIS.

[381]  Wynne Hsu,et al.  Analyzing the Subjective Interestingness of Association Rules , 2000, IEEE Intell. Syst..

[382]  Christian Jacquemin.  FASTR : A Unification-Based Front-End to Automatic Indexing , 1994, RIAO.

[383]  L.M. Sheikh,et al.  Interesting measures for mining association rules , 2004, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..

[384]  Geoffrey I. Webb,et al.  On detecting differences between groups , 2003, KDD '03.

[385]  Frans Coenen,et al.  The effect of threshold values on association rule based classification accuracy , 2007, Data Knowl. Eng..

[386]  Allan Collins,et al.  A spreading-activation theory of semantic processing , 1975 .

[387]  Alexa T. McCray.  The Unified Medical Language System. the Umls Semantic Network: The UMLS Semantic Network , 1989 .

[388]  Edson Augusto Melanda.  Post-processing of association rules , 2004 .

[389]  Ke Wang,et al.  Mining patterns that respond to actions , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[390]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[391]  Patrick Paroubek.  EVALUATING PART-OF-SPEECH TAGGING AND PARSING On the Evaluation of Automatic Parsing of Natural Language , 2007 .

[392]  Carson Kai-Sang Leung,et al.  DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams , 2006, Sixth International Conference on Data Mining (ICDM'06).

[393]  Daryl Pregibon,et al.  A Statistical Perspective on Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[394]  Haim Levkowitz,et al.  Color Theory and Modeling for Computer Graphics, Visualization, and Multimedia Applications , 1997 .

[395]  H. B. Mann.  Nonparametric Tests Against Trend , 1945 .

[396]  Miao Duo,et al.  An Information Representation of the Concepts and Operations in Rough Set Theory , 1999 .

[397]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[398]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[399]  Wynne Hsu,et al.  Pruning and summarizing the discovered associations , 1999, KDD '99.

[400]  W. W. Armstrong,et al.  Dependency Structures of Data Base Relationships , 1974, IFIP Congress.

[401]  Antony Unwin,et al.  The TwoKey Plot for Multiple Association Rules Control , 2001, PKDD.

[402]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[403]  Rakesh Agrawal,et al.  Parallel Mining of Association Rules: Design, Implementation and Experience , 1999 .

[404]  Hiep Xuan Huynh,et al.  Evaluating Interestingness Measures with Linear Correlation Graph , 2006, IEA/AIE.

[405]  Detlef D. Nauck,et al.  Towards a Framework for Change Detection in Data Sets , 2006, SGAI Conf..

[406]  Engelbert Mephu Nguifo,et al.  About the Lossless Reduction of the Minimal Generator Family of a Context , 2007, ICFCA.

[407]  Carlo Zaniolo,et al.  Verifying and Mining Frequent Patterns from Large Windows over Data Streams , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[408]  Abdelaziz Berrado,et al.  Using metarules to organize and group discovered association rules , 2006, Data Mining and Knowledge Discovery.

[409]  Osmar R. Zaïane,et al.  Learning to Use a Learned Model: A Two-Stage Approach to Classification , 2006, Sixth International Conference on Data Mining (ICDM'06).

[410]  Ivan Bruha,et al.  From machine learning to knowledge discovery: Survey of preprocessing and postprocessing , 2000, Intell. Data Anal..

[411]  Heike Hofmann,et al.  Visual Comparison of Association Rules , 2001, Comput. Stat..

[412]  Toon Calders,et al.  Mining All Non-derivable Frequent Itemsets , 2002, PKDD.

[413]  José María Carazo,et al.  BMC Bioinformatics BioMed Central Methodology article Integrated analysis of gene expression by association rules discovery , 2022 .

[414]  Yiyu Yao,et al.  An Analysis of Quantitative Measures Associated with Rules , 1999, PAKDD.

[415]  Paul D. McNicholas,et al.  Standardising the lift of an association rule , 2008, Comput. Stat. Data Anal..

[416]  Bart Baesens,et al.  Post-Processing of Association Rules , 2009 .

[417]  Jian Pei,et al.  Mining Succinct Systems of Minimal Generators of Formal Concepts , 2005, DASFAA.

[418]  Mihael Ankerst,et al.  Visual Data Mining , 2014, Encyclopedia of Social Network Analysis and Mining.

[419]  I C Lerman,et al.  Likelihood linkage analysis (LLA) classification method: an example treated by hand. , 1993, Biochimie.

[420]  Rafal Latkowski,et al.  Data Decomposition and Decision Rule Joining for Classification of Data with Missing Values , 2004, Trans. Rough Sets.

[421]  Nan Jiang,et al.  Research issues in data stream association rule mining , 2006, SGMD.

[422]  Stephen D. Bay,et al.  Detecting Group Differences: Mining Contrast Sets , 2001, Data Mining and Knowledge Discovery.

[423]  Vincent Duquenne,et al.  Familles minimales d'implications informatives résultant d'un tableau de données binaires , 1986 .

[424]  Huang-Cheng Kuo,et al.  Building a Concept Hierarchy by Hierarchical Clustering with Join/Merge Decision , 2006, JCIS.

[425]  Maria E. Orlowska,et al.  CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules , 1998, PAKDD.

[426]  Tiente Hsu,et al.  Optimizing occlusion appearances in 3D association rules visualization , 2008, 2008 4th International IEEE Conference Intelligent Systems.