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Relational mountain (density) clustering method and web log analysis
Author(s) -
Pal Kuhu,
Pal Nikhil R.,
Keller James M.,
Bezdek James C.
Publication year - 2005
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20071
Subject(s) - cluster analysis , computer science , relational database , data mining , partition (number theory) , consensus clustering , hierarchical clustering , set (abstract data type) , object (grammar) , fuzzy clustering , cure data clustering algorithm , mathematics , artificial intelligence , combinatorics , programming language
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.