Relational mountain (density) clustering method and web log analysis

Authors:
Kuhu Pal;Nikhil R. Pal;James M. Keller;James C. Bezdek
Affiliations:
Institute of Engineering and Management, Salt Lake Electronics Complex, Sector V, Calcutta 700091, India;Electronics and Communications Sciences Unit, Indian Statistical Institute, 203 BT Road, Calcutta 700035, India;Electrical and Computer Engineering Department, 217 Engineering Building West, University of Missouri-Columbia, Columbia, MO 65211-2060, USA;Computer Science Department, University of West Florida, 11000 University Parkway, Pensacola, FL 32514, USA
Venue:
International Journal of Intelligent Systems
Year:
2005

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Abstract

The mountain clustering method and the subtractive clustering method are useful methods for finding cluster centers based on local density in object data. These methods have been extended to shell clustering. In this article, we propose a relational mountain clustering method (RMCM), which produces a set of (proto) typical objects as well as a crisp partition of the objects generating the relation, using a new concept that we call relational density. We exemplify RMCM by clustering several relational data sets that come from object data. Finally, RMCM is applied to web log analysis, where it produces useful user profiles from web log data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 375–392, 2005.