Authors:
Chao Xu
1
;
Chunlin Xu
2
and
Shengli Wu
1
Affiliations:
1
School of Computer Science, Jiangsu University, Zhenjiang, China
;
2
School of Computer Science,Guangdong Polytechnic Normal University, Guangzhou, China
Keyword(s):
Evolutionary Clustering, Clustering Ensemble, Supervised Classifier.
Abstract:
Evolutionary clustering is a type of algorithm that uses genetic algorithms to optimize clustering results. Unlike traditional clustering algorithms which obtain clustering results by iteratively increasing the distance between clusters and reducing the distance between instances within a cluster, the evolutionary clustering algorithm tries to search for the optimal clustering result in the solution space. Not surprisingly, the initial population set in an evolutionary clustering algorithm has significant influence on the final results. To ensure the quality of the initial population, this paper proposed a clustering ensemble-based method, ECA-CE, to do the initial population for the evolutionary clustering algorithm. In ECA-CE, a clustering ensemble method, Hybrid Bipartite Graph Formulation, is applied. Extensive experiments are conducted on 20 benchmark datasets, and the experimental results demonstrate that the proposed ECA-CE is more effective than two evolutionary clustering al
gorithms F1-ECAC and ECAC in terms of Adjusted Rand index.
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