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Hubert, Lawrence J. Steinley, Douglas Lee 2015-09-25T20:38:57Z 2015-09-25T20:38:57Z 10000-01-01 2004 2004 "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." Made available in DSpace on 2015-09-25T20:38:57Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3131029.pdf: 8605233 bytes, checksum: 99ae9e82e66c2fb66044163cb9da0b4a (MD5) Previous issue date: 2004 Embargo set by: Seth Robbins for item 83338 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 215 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004. http://hdl.handle.net/2142/82057 (MiAaPQ)AAI3131029 eng Psychology, Psychometrics Local Optima in K-Means Clustering text Psychology Psychology University of Illinois at Urbana-Champaign Dissertation Ph.D.