Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
A comparative study of clustering methods
Future Generation Computer Systems - Special double issue on data mining
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
ITERATE: a conceptual clustering algorithm for data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A distance measure for determining similarity between criminal investigations
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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In this paper we propose a method for extracting clusters in a population of customers, where the only information available is the list of products bought by the individual clients. We use association rules having high confidence to construct a hierarchical sequence of clusters. A specific metric is introduced for measuring the quality of the resulting clusterings. Practical consequences are discussed in view of some experiments on real life datasets.