Semi-supervised model-based clustering with positive and negative constraints

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• 作者：Volodymyr Melnykov ; Igor Melnykov…
• 关键词：Semi ; supervised clustering ; Model ; based clustering ; Finite mixture models ; Positive and negative constraints ; BIC
• 刊名：Advances in Data Analysis and Classification
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：10
• 期：3
• 页码：327-349
• 全文大小：966 KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Statistics
  Statistical Theory and Methods
  Statistics for Business, Economics, Mathematical Finance and Insurance
  Statistics for Life Sciences, Medicine and Health Sciences
  Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
  Statistics for Social Science, Behavorial Science, Education, Public Policy and Law
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1862-5355
• 卷排序：10

文摘

Cluster analysis is a popular technique in statistics and computer science with the objective of grouping similar observations in relatively distinct groups generally known as clusters. Semi-supervised clustering assumes that some additional information about group memberships is available. Under the most frequently considered scenario, labels are known for some portion of data and unavailable for the rest of observations. In this paper, we discuss a general type of semi-supervised clustering defined by so called positive and negative constraints. Under positive constraints, some data points are required to belong to the same cluster. On the contrary, negative constraints specify that particular points must represent different data groups. We outline a general framework for semi-supervised clustering with constraints naturally incorporating the additional information into the EM algorithm traditionally used in mixture modeling and model-based clustering. The developed methodology is illustrated on synthetic and classification datasets. A dendrochronology application is considered and thoroughly discussed.