Article ID Journal Published Year Pages File Type
6901307 Procedia Computer Science 2017 8 Pages PDF
Abstract
We present subspace clustering method based on entropy and modification of Gustafson-Kessel clustering. The proposed method associates with each cluster its center, variance co-variance matrix and a weight matrix. It represents clusters and their features through gradation in membership, and hence reflects a realistic representation of clusters. Evaluation of experiments on data from UCI ae well as text with the comparative methods shows better results of proposed method.
Related Topics
Physical Sciences and Engineering Computer Science Computer Science (General)
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