Approximations to Distributions

Bowman, K. O.; Shenton, L. R.

doi:10.1002/0471667196.ess0055

Cited by 1 publication

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“…A first way to test the significance of s(P k , P k 0 ) can be by approximation (Bowman and Shenton (1982)). Mielke (1979) and Brook and Stirling (1984) demonstrated (by using analytical and simulation tools, respectively) the inadequacy of significance tests evaluated through a normal approximation because of the skewness in the null distribution of the Rand index; they also indicated how to obtain a distributional approximation based on at least the third moment (e.g., the Pearson Type I as suggested by Mielke et al (1976) and Mielke (1978) for a similar measure).…”

Section: A Procedures To Identify Cluster Structures In a Data Matrixmentioning

confidence: 99%

Identifying Multiple Cluster Structures in a Data Matrix

Soffritti

2003

Communications in Statistics - Simulation and Computation

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An aspect of cluster analysis which has been widely studied in recent years is the weighting and selection of variables. Procedures have been proposed which are able to identify the cluster structure present in a data matrix when that structure is confined to a subset of variables. Other methods assess the relative importance of each variable as revealed by a suitably chosen weight. But when a cluster structure is present in more than one subset of variables and is different from one subset to another, those solutions as well as standard clustering algorithms can lead to misleading results. Some very recent methodologies for finding consensus classifications of the same set of units can be useful also for the identification of cluster structures in a data matrix, but each one seems to be only partly satisfactory for

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Section: A Procedures To Identify Cluster Structures In a Data Matrixmentioning

confidence: 99%

Identifying Multiple Cluster Structures in a Data Matrix

Soffritti

2003

Communications in Statistics - Simulation and Computation

View full text Add to dashboard Cite

show abstract

Approximations to Distributions

Cited by 1 publication

References 32 publications

Identifying Multiple Cluster Structures in a Data Matrix

Identifying Multiple Cluster Structures in a Data Matrix

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