藉由K均值分群與分裂式分群程序預測潛在群體

Abstract

本研究的主要目的是藉由群聚分析的方法預測潛在群體。利用群聚方法中的k均值分群和分裂階層分群的想法，將原本的距離測度改為相關係數或共變異數，對所有的主體分群，使得屬於同一群的主體所測得的各項目能互相獨立。利用模擬來評估參數估計的表現，除此之外，還利用精神分裂症和乳癌的微陣列資料為例，作更詳細的說明。模擬結果顯示，k 均值分群法所估出來的參數都相當靠近真實的參數，但是分裂式階層分群法表現得並不好;然而，在乳癌資料的例子裡，分裂階層分群法成功的將主體分群，也對潛在群體做了不錯的預測。

The aim of the study is to predict the underlying latent class via k-means and divisive hierarchical clustering methods. We use the correlation (or covariance) among items as the distance measure to group objects such that, for all objects who belong to the same latent class, items are ”independent”. A simulation study is presented to evaluate the behavior of estimating parameters. Besides, the schizophrenia and breast cancer microarray data were used for illustration. The results of the simulation studies displayed that the estimated parameters by k-means method are closed to the true parameters, but the divisive hierarchical method didn’t perform well. However, the divisive hierarchical approach makes the successful division and predicts the latent class membership well for breast cancer data.