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ReCoM: Reinforcement Clustering of
Multi-Type Interrelated Data Objects
April 1, 2003
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Download PDF
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BibTex
Authors

Jidong Wang

Huajun Zeng

Zheng Chen

Hong-Jun Lu

Li Tao

Wei-Ying Ma

Hong-Jiang Zhang
Publication Type
TechReport
Pages
9
Number
MSR-TR-2003-25

Abstract

Related Info
Abstract
Most existing clustering algorithms cluster highly related data objects such as Web pages and
Web users separately. The interrelation among different types of data objects is either not
considered, or represented by a static feature space and treated in the same ways as other
attributes of the objects. In this paper, we propose a novel clustering approach for clustering
multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type
Interrelated data objects). Under this approach, relationships among data objects are used to
improve the cluster quality of interrelated data objects through an iterative reinforcement
clustering process. At the same time, the link structure derived from relationships of the
interrelated data objects is used to differentiate the importance of objects and the learned
importance is also used in the clustering process to further improve the clustering results.
Experimental results show that the proposed approach not only effectively overcomes the
problem of data sparseness caused by the high dimensional relationship space but also
significantly improves the clustering accuracy.
Related Info
Related Files
 tr-2003-25.doc
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 Microsoft Research Lab - Asia
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