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Title:Local Optima in K-Means Clustering
Author(s):Steinley, Douglas Lee
Doctoral Committee Chair(s):Hubert, Lawrence J.
Department / Program:Psychology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Psychology, Psychometrics
Abstract:The study of the properties of local optimality in K-means clustering is pursued. In doing so, it is shown that several of the commercial software packages prove to be inadequate in their treatment of the K-means algorithm, resulting in the proposal of an alternative method based on several thousand initializations, which is imbedded in a MATLAB m-file. The further developments of this dissertation are four-fold: (a) a comprehensive cluster generation method based on distributional theory and probability is developed; (b) the properties of local optimality are related to a cluster recovery criterion to develop a test that is able to distinguish between "good" and "bad" cluster solutions; (c) a method of consensus analysis for K-means clustering is proposed and extended to within-cluster standardization; and (d) a lower bound for the K -means criterion function is derived, and based on the lower bound, another (more powerful) test is developed to determine the quality of a given cluster solution.
Issue Date:2004
Description:215 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.
Other Identifier(s):(MiAaPQ)AAI3131029
Date Available in IDEALS:2015-09-25
Date Deposited:2004

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