Fast discovery of association rules
Advances in knowledge discovery and data mining
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
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational Database Systems
Partitioning Nominal Attributes in Decision Trees
Data Mining and Knowledge Discovery
On Axiomatization of Conditional Entropy of Functions Between Finite Sets
ISMVL '99 Proceedings of the Twenty Ninth IEEE International Symposium on Multiple-Valued Logic
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We generalize the notion of entropy for a set of attributes of a table and we study its applications to clustering of categorical data. This new concept allows greater flexibility in identifying sets of attributes and, in a certain case, is naturally related to the average distance between the records that are the object of clustering. An algorithm that identifies clusterable sets of attributes (using several types of entropy) is also presented as well as experimental results obtained with this algorithm.