Finding Rare Patterns with Weak Correlation Constraint: Progress in Indicative and Chance Patterns

A notion of rare patterns has been recently paid attention in several research fields including Chance Discovery, Formal Concept Analysis and Data Mining. In this paper, we overview the progress of our investigations on rare patterns satisfying a weak-correlation constraint. A rare pattern must indicate some significance as well as a fact that the number of its instances is a few. We pay our attention to a pattern as an itemset in a transaction database which consists of several general items, but has a very small degree of correlation in spite of the generality of component items. Such a pattern is called an indicative pattern and is regarded as a rare pattern to be extracted.

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

[2]  Bernhard Ganter,et al.  Formal Concept Analysis , 2013 .

[3]  Kotagiri Ramamohanarao,et al.  Using emerging patterns and decision trees in rare-class classification , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[4]  Yukio Ohsawa Discovery of Chances Underlying Real Data , 2002, Progress in Discovery Science.

[5]  Setsuo Arikawa,et al.  Progress in Discovery Science , 2002, Lecture Notes in Computer Science.

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

[7]  Samir Kouro,et al.  Unidimensional Modulation Technique for Cascaded Multilevel Converters , 2009, IEEE Transactions on Industrial Electronics.

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

[9]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

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

[11]  Tai-Wen Yue,et al.  A Q'tron Neural-Network Approach to Solve the Graph Coloring Problems , 2007 .

[12]  Makoto Haraguchi,et al.  Implicit Groups of Web Pages as Constrained Top N Concepts , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

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

[14]  Tatsuya Akutsu,et al.  Efficient Algorithms for Finding Maximum and Maximal Cliques: Effective Tools for Bioinformatics , 2011 .

[15]  Makoto Haraguchi,et al.  Pinpoint Clustering of Web Pages and Mining Implicit Crossover Concepts , 2010 .

[16]  Makoto Haraguchi,et al.  Discovery of hidden correlations in a local transaction database based on differences of correlations , 2005, Eng. Appl. Artif. Intell..

[17]  Yukio Ohsawa,et al.  KeyGraph : Automatic Indexing by Segmenting and Unifing Co-Occurrence Graphs , 1999 .

[18]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[19]  Gary M. Weiss Mining with rarity: a unifying framework , 2004, SKDD.

[20]  Amedeo Napoli,et al.  Towards Rare Itemset Mining , 2007 .

[21]  Hiroki Arimura,et al.  LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets , 2004, FIMI.

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

[23]  Luigi Troiano,et al.  A Fast Algorithm for Mining Rare Itemsets , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[24]  Makoto Haraguchi,et al.  Finding Rare Patterns with Weak Correlation Constraint , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[25]  Makoto Haraguchi,et al.  Finding Top-N Chance Patterns with KeyGraph $^{\tiny \textregistered}$ -Based Importance , 2011, KES.

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

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

[28]  Makoto Haraguchi,et al.  An Algorithm for Extracting Rare Concepts with Concise Intents , 2010, ICFCA.

[29]  Geoffrey I. Webb,et al.  Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining , 2009, J. Mach. Learn. Res..

[30]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Makoto Haraguchi,et al.  Contrasting Correlations by an Efficient Double-Clique Condition , 2012, Trans. Mach. Learn. Data Min..

[32]  M. Barber,et al.  Detecting network communities by propagating labels under constraints. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Yukio Ohsawa,et al.  Human–Computer Interactive Annealing for Discovering Invisible Dark Events , 2007, IEEE Transactions on Industrial Electronics.

[34]  Yukio Ohsawa,et al.  Discover Risky Active Faults by Indexing an Earthquake Sequence , 1999, Discovery Science.

[35]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[36]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.