Subspace Clustering Based on Self-organizing Map
Clustering in high-dimensional data space is a difficult task due to the interference from different dimensions. A dimension may be relevant for some clusters and irrelevant for other data. Subspace clustering aims at finding local cluster structures in certain related subspace. We propose a novel approach to finding subspace clusters based on the trained Self-Organizing Map neural network (SOM). The proposed method takes advantage of nonlinear mapping of SOM and search for subspace clusters on input neurons instead of the whole data space. Experiment results show that the proposed method performs better compared with original SOM and some traditional subspace clustering algorithms.
KeywordsSelf-organizing map Subspace clustering High-dimensional clustering
The work was supported by the General Program of the National Science Foundation of China (Grant No. 71471127, 71371135).
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