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PAKDD
2007
ACM

Clustering Ensembles Based on Normalized Edges

10 years 13 hour ago
Clustering Ensembles Based on Normalized Edges
The co-association (CA) matrix was previously introduced to combine multiple partitions. In this paper, we analyze the CA matrix, and address its difference from the similarity matrix using Euclidean distance. We also explore how to find a proper and better algorithm to obtain the final partition using the CA matrix. To get more robust and reasonable clustering ensemble results, a new hierarchical clustering algorithm is proposed by developing a novel concept of normalized edges to measure the similarity between clusters. The experimental results of the proposed approach are compared with those of some single runs of well-known clustering algorithms and other ensemble methods and the comparison clearly demonstrates the effectiveness of our algorithm.
Yan Li, Jian Yu, Pengwei Hao, Zhulin Li
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where PAKDD
Authors Yan Li, Jian Yu, Pengwei Hao, Zhulin Li
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