Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Comparing Subspace Clusterings
IEEE Transactions on Knowledge and Data Engineering
IBUSCA: A Grid-based Bottom-up Subspace Clustering Algorithm
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
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Subspace clustering methods seek to find clusters in different subspaces within a data set instead of searching them in full feature space. In such a case there is a problem how to evaluate the quality of the clustering results. In this paper we present our method of the subspace clustering quality estimation which is based on adaptation of Davies-Bouldin Indexto subspace clustering. The assumptions which were made to build the metrics are presented first. Then the proposed metrics is formally described. Next it is verified in an experimental way with the use of our clustering method IBUSCA. The experiments have shown that its value reflects a quality of subspace clustering thus it can be an alternative in the case where there is no expert's evaluation.