標題: Locality Sensitive K-means Clustering
作者: Liu, Chlen-Liang
Hsai, Wen-Hoar
Chang, Tao-Hsing
資訊工程學系
工業工程與管理學系
Department of Computer Science
Department of Industrial Engineering and Management
關鍵字: unsupervised learning;clustering;locality sensitive clustering;dimensionality reduction;linear transformation
公開日期: 1-一月-2018
摘要: This study considers clustering and dimensionality reduction simultaneously to devise an unsupervised clustering algorithm called locality sensitive K-means (LS-K means). The goal is to find a linear transformation to project data points into a lower dimensional space, so that clustering can perform well in the new space. We design a novel objective function for LS-Kmeans to achieve the goal, and further show that the proposed method can be reformulated as a matrix trace minimization with constraints problem. The original optimization problem becomes a generalized eigenvalue problem when relaxing the optimization problem of LS-Kmeans by allowing the indicator entries to take arbitrary values in R. This paper also shows that the continuous solutions for the transformed cluster membership indicator vectors of LS-Kmeans are located in the subspace spanned by the first K-1 eigenvectors. In the experiments, we use two synthetic datasets to show that the proposed method can cluster non-linearly separable data points. Besides, the experimental results of eight real datasets indicate that the proposed algorithm can generally outperform other alternatives.
URI: http://dx.doi.org/10.6688/JISE.2018.34.1.17
http://hdl.handle.net/11536/144422
ISSN: 1016-2364
DOI: 10.6688/JISE.2018.34.1.17
期刊: JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
Volume: 34
起始頁: 289
結束頁: 305
顯示於類別:期刊論文