论文信息 - Deriving Class Association Rules Based on Levelwise Subspace Clustering

Deriving Class Association Rules Based on Levelwise Subspace Clustering

Most approaches of Class Association Rule (CAR) based classification have not intensively addressed the classification of instances including numeric attributes. In this paper, a levelwise subspace clustering method deriving hyper-rectangular clusters is proposed to efficiently provide quantitative, interpretative and accurate CARs.

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

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

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

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

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

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

[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. Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

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