A Classification Method Based on Subspace Clustering and Association Rules

Class Association Rule (CAR) based classification is known to provide high interpretability

[1]  Philip S. Yu,et al.  Clustering through decision tree construction , 2000, CIKM '00.

[2]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

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

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

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

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

[7]  T. M. Murali,et al.  A Monte Carlo algorithm for fast projective clustering , 2002, SIGMOD '02.

[8]  Hans-Peter Kriegel,et al.  Density-Connected Subspace Clustering for High-Dimensional Data , 2004, SDM.

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

[10]  Ke Wang,et al.  Interestingness-Based Interval Merger for Numeric Association Rules , 1998, KDD.

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

[12]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[13]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.