Gene transposon based clonal selection algorithm for clustering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Multi-objective optimization for dynamic single-machine scheduling
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Gene transposon based clone selection algorithm for automatic clustering
Information Sciences: an International Journal
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
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Most existing clustering methods require prior knowledge, such as the number of clusters and thresholds. They are difficult to determine accurately in practice. To solve the problem, this study proposes a novel clustering algorithm named GEP-Cluster based on Gene Expression Programming (GEP) without prior knowledge. The main contributions include: (1) a new concept named Clustering Algebra is proposed that makes clustering as algebraic operation, (2) a GEP-Cluster algorithm is proposed to find the best clustering information automatic by GEP and discover the best clustering solution without any prior knowledge, (3) an AMCA (Automatic Merging Cluster Algorithm) algorithm is proposed to merge clustering automatically. Extensive experiments demonstrate that GEP-Cluster algorithm is effective in clustering without any prior knowledge on various data sets.