Comparative study of matrix refinement approaches for ensemble clustering

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• 作者：Natthakan Iam-On (1)
  Tossapon Boongoen (2)
  
  1. School of Information Technology ; Mae Fah Luang University ; Chiang Rai ; 57100 ; Thailand
  2. Department of Mathematics and Computer Science ; Royal Thai Air Force Academy ; Bangkok ; 10220 ; Thailand
  
• 关键词：Cluster ensemble ; Multiple clusterings ; Summarization ; Information matrix
• 刊名：Machine Learning
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：98
• 期：1-2
• 页码：269-300
• 全文大小：4,245 KB
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• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Automation and Robotics
  Computing Methodologies
  Simulation and Modeling
  Language Translation and Linguistics
  
• 出版者：Springer Netherlands
• ISSN：1573-0565

文摘

Cluster ensembles or consensus clusterings have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across various sets of data. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique. Since founded, different research areas have emerged with the common purpose of enhancing the effectiveness and applicability of cluster ensembles. These include the selection of ensemble members, the imputation of missing values, and the summarization of ensemble members. In particular, this paper is set to provide the review of different matrix refinement approaches that have been recently proposed in the literature for summarizing information of multiple clusterings. With various benchmark datasets and quality measures, the comparative study of these novel techniques is carried out to provide empirical findings from which a practical guideline can be drawn.