PCA-Guided k-Means with Variable Weighting and Its Application to Document Clustering
作者： Katsuhiro Honda , Akira Notsu , Hidetomo Ichihashi
DOI: 10.1007/978-3-642-04820-3_26
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摘要: PCA-guided k -Means is a deterministic approach to clustering, in which cluster indicators are derived manner. This paper proposes new with variable selection by introducing weighting mechanism into -Means. The relative responsibility of variables estimated similar way FCM clustering while the membership indicator from manner, principal component scores calculated considering weights variables. So, that have meaningful information for capturing structures emphasized calculation indicators. Numerical experiments including an application document demonstrate characteristics proposed method.
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