A Graph Based Clustering Method using a Hybrid Evolutionary Algorithm
نویسندگان
چکیده
Clustering of data items is one of the important applications of graph partitioning using a graph model. The pairwise similarities between all data items form the adjacency matrix of a weighted graph that contains all the necessary information for clustering. In this paper we propose a novel hybrid-evolutionary algorithm based on graph partitioning approach for data clustering. The algorithm is currently tested on synthetic datasets to allow controlled experiments and the results show that our method can effectively cluster data items. Key-words: Sequence clustering, pairwise similarity, evolutionary optimization, evolutionary algorithms, estimation of distribution algorithms
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