Clustering with Multiviewpoint-Based Similarity Measure

All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multiviewpoint-based similarity measure and two related clustering...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2012-06, Vol.24 (6), p.988-1001
Main Authors: Nguyen, Duc Thang, Chen, Lihui, Chan, Chee Keong
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Assessments
Cluster analysis
Clustering
Clustering algorithms
Clusters
Collection
Criteria
Current measurement
Document clustering
Euclidean distance
Origins
Partitioning algorithms
Proposals
Similarity
similarity measure
Strontium
Studies
text mining
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Summary:All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multiviewpoint-based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2011.86