CLUSTERING NOMINAL DATA WITH EQUIVALENT CATEGORIES

DOI PDF Web Site 参考文献21件

この論文をさがす

説明

The problem considered in the present paper is how to cluster data of nominal measurement level, where the categories of the variables are equivalent (the variables are replications of each other). One suitable technique to obtain such a clustering is latent class analysis (LCA) with equality restrictions on the conditional probabilities. As an alternative, a less well known technique is introduced: GROUPALS. This is an algorithm for the simultaneous scaling (by multiple correspondence analysis) and clustering of categorical variables. Equality restrictions on the category quantifications were incorporated in the algorithm, to account for equivalent categories. In two simulation studies, the clustering performance was assessed by measuring the recovery of true cluster membership of the individuals. The effect of several systematically varied data features was studied. Restricted LCA obtained good to excellent cluster recovery results. Restricted GROUPALS approximated this optimal performance reasonably well, except when underlying classes were very different in size.

収録刊行物

  • Behaviormetrika

    Behaviormetrika 35 (1), 35-54, 2007

    日本行動計量学会

参考文献 (21)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ