A clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction

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• 作者：Sirisup Laohakiat ; ^{sirisup.l@gmail.com} ; Suphakant Phimoltares ; Chidchanok Lursinsap
• 关键词：Stream data clustering ; Dimension reduction ; Linear discriminant analysis ; Clustering algorithm ; Linear discriminant analysis subspace
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：381
• 期：Complete
• 页码：104-123
• 全文大小：1598 K
• 卷排序：381

文摘

We present an algorithm for clustering high dimensional streaming data. The algorithm incorporates dimension reduction into the stream clustering framework. When a new datum arrives, the algorithm performs dimension reduction to find a local projected subspace using unsupervised LDA (Linear Discriminant Analysis)-based method. The obtained local subspace would maximally separate the nearby micro-clusters with respect to the incoming point. Then, the incoming point is assigned to a micro-cluster in the projected space, rather than in the full dimensional space. The experimental results show that the proposed algorithm outperforms its counterpart streaming clustering algorithms. Moreover, when compared with traditional clustering algorithms which require the whole data set, the proposed algorithms shows comparable clustering performances with much less computation time for large data sets.