Cluster analysis: Survey and evaluation of techniques by E. J. Bijnen (auth.)

By E. J. Bijnen (auth.)

During the final years the variety of functions of cluster research within the social sciences has elevated very speedily. one of many purposes for this is often the growing to be expertise that the idea of homogeneity implicit within the software of such ideas as issue research and scaling is usually violated via social technological know-how facts; one other is the elevated curiosity in typolo­ gies and the development of sorts. Dr. Bijnen has performed a very invaluable task via placing jointly and comparing makes an attempt to reach at larger and extra dependent concepts of cluster research from such varied fields because the social sciences, biology and drugs. His presentation is especially transparent and concise, reflecting his goal to not write a 'cookery-book' yet a textual content for students who want a trustworthy advisor to pilot them via an in depth and commonly scattered literature. Ph. C. Stouthard v Preface This booklet features a survey of a couple of innovations of clustering research. The benefits and demerits of the tactics defined also are mentioned in order that the examine employee could make an educated selection be­ tween them. those options were released in a really nice variety of journals which aren't all simply obtainable to the sociologist. This trouble is com­ pounded simply because advancements within the varied disciplines have happened virtually totally independently from one another; reference is made merely sporadically in a section of literature to the literature of alternative disciplines.

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Extra info for Cluster analysis: Survey and evaluation of techniques

Example text

A decline in the value of B for a certain variable does not mean that the analysis should be stopped, but that B should first be computed for other possibilities. This implies that a variable which has been excluded in the beginning can be added if it turns up last. 8. 1. Wherry and Gaylord (1943) have indicated a method for forming subsets of variables which are supposed to show great similarity with factors in the factor analysis. me iterative factor analysis to this method. For dichotomous data (0, 1) the procedure is as follows (ibid.

3. Boon van Ostade (1963) has introduced some changes into the Wherry and Gaylord method. He takes his point of departure from dichotomous data with 50% plus (= 1) and 50% min-scores (=0). The total score is computed for each person. The frequency distribution of the total scores is separated into high and low parts in such a fashion that both parts have exactly the same size (eventually to be provided with random numbers). The correlation cf> between each variable and the dichotomised total score (this is the product moment correlation coefficient for a 2 x 2 table) is computed.

Of the ratio between the average intercorrelation of the variables in one cluster and the average correlation with the variables outside that cluster (Harman, 1960, p. ). Hence two criteria are united in B: on the one hand the similarity in a cluster (as high as possible) and on the other hand the similarity between the clusters (as low as possible). The analysis begins with the two variables having the highest correlation. To those two a third variable, which has the highest average correlation with those already assembled, is added, etc.

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