Mining Conditional Contrast Patterns

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[19]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

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

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

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