Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
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
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
On Clustering Validation Techniques
Journal of Intelligent Information Systems
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We propose in this paper a subspace clustering in high dimensional datasets using an iterative evolutionary algorithm. The evolutionary algorithm offers an original alternative to solve the problem of selecting subspace to deal with complex data structures in different subspaces. These subspaces that shows specific data structures are chosen iteratively. This step allows us to cluster data and subspace simultaneously. The evolutionary approach evaluates a subset of dimensions with a measure to find the best data cluster and then repeat the process for several subsets selected iteratively by the evolutionary algorithm. Experiments and comparisons on data sets with a large number of measurements show the effectiveness of the proposed approach.