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Stability analysis in K ‐means clustering
Author(s) -
Steinley Douglas
Publication year - 2008
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711007x184849
Subject(s) - cluster analysis , stability (learning theory) , cluster (spacecraft) , quadratic equation , set (abstract data type) , matrix (chemical analysis) , mathematics , object (grammar) , data set , computer science , mathematical optimization , algorithm , data mining , statistics , artificial intelligence , machine learning , materials science , geometry , composite material , programming language
This paper develops a new procedure, called stability analysis, for K ‐means clustering. Instead of ignoring local optima and only considering the best solution found, this procedure takes advantage of additional information from a K ‐means cluster analysis. The information from the locally optimal solutions is collected in an object by object co‐occurrence matrix. The co‐occurrence matrix is clustered and subsequently reordered by a steepest ascent quadratic assignment procedure to aid visual interpretation of the multidimensional cluster structure. Subsequently, measures are developed to determine the overall structure of a data set, the number of clusters and the multidimensional relationships between the clusters.