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Cluster Ensemble Selection
Author(s) -
Fern Xiaoli Z.,
Lin Wei
Publication year - 2008
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.10008
Subject(s) - selection (genetic algorithm) , computer science , ensemble learning , cluster analysis , cluster (spacecraft) , machine learning , data mining , quality (philosophy) , artificial intelligence , diversity (politics) , programming language , philosophy , epistemology , sociology , anthropology
This paper studies the ensemble selection problem for unsupervised learning. Given a large library of different clustering solutions, our goal is to select a subset of solutions to form a smaller yet better‐performing cluster ensemble than using all available solutions. We design our ensemble selection methods based on quality and diversity, the two factors that have been shown to influence cluster ensemble performance. Our investigation revealed that using quality or diversity alone may not consistently achieve improved performance. Based on our observations, we designed three different selection approaches that jointly consider these two factors. We empirically evaluated their performance in comparison with both full ensembles and a random selection strategy. Our results indicate that by explicitly considering both quality and diversity in ensemble selection, we can achieve statistically significant performance improvement over full ensembles. Copyright © 2008 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000‐000, 2008