A fragment-based iterative consensus clustering algorithm with a robust similarity

The consensus clustering technique combines multiple clustering results without accessing the original data. Consensus clustering can be used to improve the robustness of clustering results or to obtain the clustering results from multiple data sources. In this paper, we propose a novel definition o...

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Bibliographic Details
Published in:Knowledge and information systems 2014-12, Vol.41 (3), p.591-609
Main Authors: Chung, Chih-Heng, Dai, Bi-Ru
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Applied sciences
Cluster analysis
Clustering
Clusters
Computer Science
Computer science
control theory
systems
Data Mining and Knowledge Discovery
Data processing. List processing. Character string processing
Data sources
Database Management
Datasets
Exact sciences and technology
Fragmentation
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Memory organisation. Data processing
Regular Paper
Robustness
Similarity
Software
Studies
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Summary:The consensus clustering technique combines multiple clustering results without accessing the original data. Consensus clustering can be used to improve the robustness of clustering results or to obtain the clustering results from multiple data sources. In this paper, we propose a novel definition of the similarity between points and clusters. With an iterative process, such a definition of similarity can represent how a point should join or leave a cluster clearly, determine the number of clusters automatically, and combine partially overlapping clustering results. We also incorporate the concept of “clustering fragment” into our method for increased speed. The experimental results show that our algorithm achieves good performances on both artificial data and real data.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-013-0667-1