Clustering by Similarity in an Auxiliary Space

Abstract. We present a clustering method for continuous data. It defines local clusters into the (primary) data space but derives its similarity measure from the posterior distributions of additional discrete data that occur as pairs with the primary data. As a case study, enterprises are clustered by deriving the similarity measure from bankruptcy sensitivity. In another case study, a content-based clustering for text documents is found by measuring differences between their metadata (keyword distributions). We show that minimizing our Kullback

• Type: Conference
• Year: 2000
• Where: IDEAL
• Authors: Janne Sinkkonen, Samuel Kaski
• Keywords: Additional Discrete Data, Case Study, IDEAL 2000, Intelligent Agents, Similarity Measure