Advanced Data Mining Techniques

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining.Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.

[1]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[2]  K. G. Srinivasa,et al.  Dynamic Association Rule Mining using Genetic Algorithms , 2005, Intell. Data Anal..

[3]  Zdziss Law Pawlak,et al.  Rough Sets and Decision Analysis , 2008 .

[4]  T.B. Trafalis,et al.  Kernel principal component analysis and support vector machines for stock price prediction , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[5]  Ada Wai-Chee Fu,et al.  Mining association rules with weighted items , 1998, Proceedings. IDEAS'98. International Database Engineering and Applications Symposium (Cat. No.98EX156).

[6]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[7]  Dongsong Zhang,et al.  Discovering golden nuggets: data mining in financial application , 2004, IEEE Trans. Syst. Man Cybern. Part C.

[8]  Barry Berman,et al.  Data mining: On the trail to marketing gold , 2004 .

[9]  Man Hon Wong,et al.  Mining fuzzy association rules in databases , 1998, SGMD.

[10]  Gary W. Loveman,et al.  Diamonds in the Data Mine , 2003 .

[11]  Salvatore Orlando,et al.  Fast and memory efficient mining of frequent closed itemsets , 2006, IEEE Transactions on Knowledge and Data Engineering.

[12]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[13]  Sholom M. Weiss,et al.  Lightweight Document Matching for Help-Desk Applications , 2000, IEEE Intell. Syst..

[14]  Evangelos Simoudis,et al.  Reality Check for Data Mining , 1996, IEEE Expert.

[15]  WLADIMIR RODRIGUEZ,et al.  Geometric Approach to Data Mining , 2001, Int. J. Image Graph..

[16]  Daniel Neagu,et al.  Fuzzy Knnmodel Applied to Predictive Toxicology Data Mining , 2005, Int. J. Comput. Intell. Appl..

[17]  Hongjun Lu,et al.  Effective Data Mining Using Neural Networks , 1996, IEEE Trans. Knowl. Data Eng..

[18]  Ping Zhang On the Distributional Properties of Model Selection Criteria , 1992 .

[19]  Keith C. C. Chan,et al.  Classification with degree of membership: a fuzzy approach , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[20]  Keith C. C. Chan,et al.  Mining fuzzy rules in a donor database for direct marketing by a charitable organization , 2002, Proceedings First IEEE International Conference on Cognitive Informatics.

[21]  Bart Baesens,et al.  Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms , 2007, Eur. J. Oper. Res..

[22]  Zdzisław Pawlak,et al.  Rough sets based decision algorithm for treatment of duodenal ulcer by HSV , 1987 .

[23]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[24]  Etienne Barnard,et al.  Data characteristics that determine classifier performance , 2006 .

[25]  Oleg I. Larichev,et al.  An approach to ordinal classification problems , 1994 .

[26]  David L. Dowe,et al.  MML Inference of Oblique Decision Trees , 2004, Australian Conference on Artificial Intelligence.

[27]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[28]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[29]  Jean-Marc Petit,et al.  Functional and approximate dependency mining: database and FCA points of view , 2002, J. Exp. Theor. Artif. Intell..

[30]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[31]  Gerrit K. Janssens,et al.  Data mining with genetic algorithms on binary trees , 2003, Eur. J. Oper. Res..

[32]  Ivan Bruha,et al.  Genetic learner: Discretization and fuzzification of numerical attributes , 2000, Intell. Data Anal..

[33]  Tzung-Pei Hong,et al.  Determining appropriate membership functions to simplify fuzzy induction , 2000, Intell. Data Anal..

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

[35]  Wooju Kim,et al.  Combination of multiple classifiers for the customer's purchase behavior prediction , 2003, Decis. Support Syst..

[36]  Z. Pawlak,et al.  Decision analysis using rough sets , 1994 .

[37]  Sung-Shun Weng,et al.  The Study and Verification of Mathematical Modeling for Customer Purchasing Behavior , 2006, J. Comput. Inf. Syst..

[38]  Dimitrios I. Fotiadis,et al.  An association rule mining-based methodology for automated detection of ischemic ECG beats , 2006, IEEE Transactions on Biomedical Engineering.

[39]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[40]  Dursun Delen,et al.  Forecasting gaming referenda , 2005 .

[41]  Richi Nayak,et al.  A Data Mining Application Analysis of Problems Occurring during a Software Project Development Process , 2005, Int. J. Softw. Eng. Knowl. Eng..

[42]  Maurizio d’Amato Appraising property with rough set theory , 2002 .

[43]  Reinhold Decker,et al.  Market basket analysis with neural gas networks and self-organising maps , 2003 .

[44]  C. Da Cunha,et al.  Data mining for improvement of product quality , 2006 .

[45]  Vojislav Kecman,et al.  Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning , 2006, Studies in Computational Intelligence.

[46]  Rokia Missaoui,et al.  Generating frequent itemsets incrementally: two novel approaches based on Galois lattice theory , 2002, J. Exp. Theor. Artif. Intell..

[47]  Ah Chung Tsoi,et al.  Pattern discovery on Australian medical claim data - a systematic approach , 2005, IEEE Transactions on Knowledge and Data Engineering.

[48]  Robert L. Grossman,et al.  Data mining standards initiatives , 2002, CACM.

[49]  Horng Fu Chuang,et al.  Rough-set-based approach to manufacturing process document retrieval , 2006 .

[50]  Usama M. Fayyad,et al.  Knowledge Discovery in Databases: An Overview , 1997, ILP.

[51]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[52]  N. Singpurwalla,et al.  Membership Functions and Probability Measures of Fuzzy Sets , 2004 .

[53]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

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

[55]  Huimin Zhao,et al.  A multi-objective genetic programming approach to developing Pareto optimal decision trees , 2007, Decis. Support Syst..

[56]  Constantin Zopounidis,et al.  Application of the Rough Set Approach to Evaluation of Bankruptcy Risk , 1995 .

[57]  Li Pheng Khoo,et al.  Feature extraction using rough set theory and genetic algorithms--an application for the simplification of product quality evaluation , 2002 .

[58]  Waldemar Karwowski,et al.  Classification of jobs with risk of low back disorders by applying data mining techniques , 2004 .

[59]  Yen-Liang Chen,et al.  Market basket analysis in a multiple store environment , 2005, Decis. Support Syst..

[60]  Shusaku Tsumoto,et al.  Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model , 2004, Inf. Sci..

[61]  BhattacharyyaSiddhartha,et al.  Adequacy of training data for evolutionary mining of trading rules , 2004 .

[62]  Thomas K. L. Tong,et al.  Rough set theory for distilling construction safety measures , 2006 .

[63]  Aboul Ella Hassanien,et al.  Intelligent data analysis of breast cancer based on rough set theory , 2003, Int. J. Artif. Intell. Tools.

[64]  Ashley C. Lovell,et al.  Using data mining to detect crop insurance fraud: is there a role for social scientists? , 2005 .

[65]  Ronald R. Yager,et al.  On Linguistic Summaries of Data , 1991, Knowledge Discovery in Databases.

[66]  Wang Rong,et al.  A genetic algorithm methodology for data mining and intelligent knowledge acquisition , 2001 .

[67]  Sunita Sarawagi,et al.  Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications , 1998, SIGMOD '98.

[68]  Tzung-Pei Hong,et al.  Trade-off Between Computation Time and Number of Rules for Fuzzy Mining from Quantitative Data , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[69]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[70]  S. Raghavan,et al.  Diversification for better classification trees , 2006, Comput. Oper. Res..

[71]  Jon M. Kleinberg,et al.  Mining the Web's Link Structure , 1999, Computer.

[72]  B. Brown,et al.  Concepts and Techniques , 1983 .

[73]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[74]  Padhraic Smyth,et al.  Business applications of data mining , 2002, CACM.

[75]  Wojtek Michalowski,et al.  Mobile clinical support system for pediatric emergencies , 2003, Decis. Support Syst..

[76]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[77]  Attila Gyenesei Mining Weighted Association Rules for Fuzzy Quantitative Items , 2000, PKDD.

[78]  Aris Pagourtzis,et al.  Computing Frequent Itemsets in Parallel Using Partial Support Trees , 2005, PVM/MPI.

[79]  Thomas Reutterer,et al.  An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data , 2003 .

[80]  PeiJian,et al.  Mining Frequent Patterns without Candidate Generation , 2000 .

[81]  Zhengxin Chen,et al.  Classifying Credit Card Accounts for Business Intelligence and Decision Making: a Multiple-criteria Quadratic Programming Approach , 2005, Int. J. Inf. Technol. Decis. Mak..

[82]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[83]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[84]  Korris Fu-Lai Chung,et al.  Fuzzy taxonomy, quantitative database and mining generalized association rules , 2005, Intell. Data Anal..

[85]  Yiming Yang,et al.  An Evaluation of Statistical Approaches to Text Categorization , 1999, Information Retrieval.

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

[87]  Wan-Jui Lee,et al.  Discovery of fuzzy temporal association rules , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[88]  M. Beynon,et al.  Explaining the Diffusion of Medicaid Home Care Waiver Programs Using VPRS Decision Rules , 2004, Health care management science.

[89]  James Nga-Kwok Liu,et al.  iJADE Web-miner: an intelligent agent framework for Internet shopping , 2004, IEEE Transactions on Knowledge and Data Engineering.

[90]  Roy Ladner,et al.  Fuzzy Set Approaches to Spatial Data Mining of Association Rules , 2003, Trans. GIS.

[91]  Siddhartha Bhattacharyya,et al.  Adequacy of training data for evolutionary mining of trading rules , 2004, Decis. Support Syst..

[92]  David L. Olson,et al.  Rule induction in data mining: effect of ordinal scales , 2002, Expert Syst. Appl..

[93]  Heikki Mannila,et al.  A database perspective on knowledge discovery , 1996, CACM.

[94]  Eyke Hüllermeier,et al.  A systematic approach to the assessment of fuzzy association rules , 2006, Data Mining and Knowledge Discovery.

[95]  Hemant K. Bhargava,et al.  Data Mining by Decomposition: Adaptive Search for Hypothesis Generation , 1999, INFORMS J. Comput..

[96]  Rung-Fang Chang,et al.  Load profile assignment of low voltage customers for power retail market applications , 2003 .

[97]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[98]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[99]  David L. Olson,et al.  An Experiment with Fuzzy Sets in Data Mining , 2007, International Conference on Computational Science.

[100]  William Nick Street,et al.  An intelligent system for customer targeting: a data mining approach , 2004, Decis. Support Syst..

[101]  Chengqi Zhang,et al.  ON DATA STRUCTURES FOR ASSOCIATION RULE DISCOVERY , 2007, Appl. Artif. Intell..

[102]  Richard Reed,et al.  Household Ethnicity, Household Consumption: Commodities and the Guaraní , 1995, Economic Development and Cultural Change.

[103]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[104]  S. Daskalaki,et al.  Data mining for decision support on customer insolvency in telecommunications business , 2003, Eur. J. Oper. Res..

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

[106]  Simon Kasif,et al.  On reasoning from data , 1995, CSUR.

[107]  Tansel Özyer,et al.  Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening , 2007, J. Netw. Comput. Appl..

[108]  Dursun Delen,et al.  Determining the Efficacy of Data-Mining Methods in Predicting Gaming Ballot Outcomes , 2006 .

[109]  Han Tong Loh,et al.  Applying rough sets to market timing decisions , 2004, Decis. Support Syst..

[110]  Jirachai Buddhakulsomsiri,et al.  Association rule-generation algorithm for mining automotive warranty data , 2006 .

[111]  Timothy G. Roche Expect increased adoption rates of certain types of EHRs, EMRs , 2006 .

[112]  Gary J. Russell,et al.  Analysis of cross category dependence in market basket selection , 2000 .

[113]  Huan Liu,et al.  Symbolic Representation of Neural Networks , 1996, Computer.

[114]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[115]  Flavia Hendler,et al.  Revenue management in fabulous Las Vegas: Combining customer relationship management and revenue management to maximise profitability , 2004 .

[117]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[118]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[119]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[120]  David L. Olson,et al.  Introduction to Business Data Mining , 2005 .