An optimal method for data clustering

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• 作者：Linsen Xie ; Chengbo Lu ; Ying Mei ; Hong Du ; Zhihong Man
• 关键词：Clustering ; Extreme learning machine ; Feature space ; Graph Laplacian
• 刊名：Neural Computing & Applications
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：27
• 期：2
• 页码：283-289
• 全文大小：531 KB
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• 作者单位：Linsen Xie (1)
  Chengbo Lu (1)
  Ying Mei (1)
  Hong Du (1)
  Zhihong Man (2)
  
  1. Department of Mathematics, Lishui University, Lishui, 323000, Zhejiang, China
  2. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC, 3122, Australia
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

An algorithm for optimizing data clustering in feature space is studied in this work. Using graph Laplacian and extreme learning machine (ELM) mapping technique, we develop an optimal weight matrix W for feature mapping. This work explicitly performs a mapping of the original data for clustering into an optimal feature space, which can further increase the separability of original data in the feature space, and the patterns points in same cluster are still closely clustered. Our method, which can be easily implemented, gets better clustering results than some popular clustering algorithms, like k-means on the original data, kernel clustering method, spectral clustering method, and ELM k-means on data include three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, and Wine database). Keywords Clustering Extreme learning machine Feature space Graph Laplacian