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ReCoM
- Source :
- SIGIR
- Publication Year :
- 2003
- Publisher :
- ACM, 2003.
-
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.
- Subjects :
- Clustering high-dimensional data
Fuzzy clustering
Brown clustering
business.industry
Computer science
Feature vector
Correlation clustering
Conceptual clustering
Constrained clustering
Pattern recognition
computer.software_genre
Data stream clustering
CURE data clustering algorithm
Consensus clustering
Canopy clustering algorithm
FLAME clustering
Data mining
Artificial intelligence
business
Cluster analysis
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
- Accession number :
- edsair.doi...........d618bd5f074a691b3be93c1629bff919