Factors affecting the character stability of classifications

Burgman, Mark A.; Sokal, Robert R.

doi:10.1007/bf00936547

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Bryant (2002) developed a method to determine the general stability of the data with respect to the choice of hierarchical clustering methods. Alternative methods are concerned with character stability: the resistance of a classification when characters are added to or removed from the system, previously found to be related to the amount of characters added or removed relative to the total number of observed units (Burgman & Sokal 1989; Cheng & Milligan, 1996; Jolliffe, Jones, & Morgan, 1988; Sokal, 1983). McIntyre and Blashfield (1980) defined stability as the ability of a cluster solution to be continually recognized in different random samples of the general population.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis in K‐means clustering

Steinley

2008

Brit J Math & Statis

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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.

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