Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Machine Learning - Special issue on learning with probabilistic representations
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Efficient Density-Based Clustering of Complex Objects
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Deriving class association rules based on levelwise subspace clustering
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Mutagenicity analysis of chemical compounds is crucial for the cause investigation of our modern diseases including cancers. For the analysis, accurate and comprehensive classification of the mutagenicity is strongly needed. Especially, use of appropriate features of the chemical compounds plays a key role for the interpretability of the classification results. In this paper, a classification approach named “Levelwise Subspace Clustering based Classification by Aggregating Emerging Patterns (LSC-CAEP)” which is known to be accurate and provides interpretable rules is applied to a mutagenicity data set. Promising results of the analysis are shown through a demonstration.