A Personalized Document Clustering Approach to Addressing Individual Categorization Preferences

As electronic commerce and knowledge economy environments proliferate, both individuals and organizations increasingly generate and consume large amounts of online information, typically available as textual documents. To manage this ever-increasing volume of documents, such individuals and organizations frequently organize their documents into categories that facilitate document management and subsequent information access and browsing. However, document clustering is an intentional act that reflects individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document clustering must consider individual preferences and needs to support personalization in document categorization. In this paper, we present an automatic document clustering approach that incorporates an individual’s partial clustering as preferential information. Combining two document representation methods with two clustering processes, we establish four personalized document clustering techniques. Using a traditional content-based document clustering technique as a performance benchmark, we find that the proposed personalized document clustering techniques improve clustering effectiveness, as measured by cluster precision and cluster recall.