Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
FGKA: a Fast Genetic K-means Clustering Algorithm
Proceedings of the 2004 ACM symposium on Applied computing
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
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In this study, we present multi-objective genetic algorithm based iterative clustering approach. Two objectives are employed in the process: minimizing the within cluster similarity and maximizing the difference between the clusters: inter-cluster distance (average linkage, centroid linkage, complete linkage and average to centroid linkage) versus intra-cluster distance (total within cluster variation). The proposed approach is iterative in the sense that it basically tries possible partitioning of the dataset for the given range of clusters one by one; the result of the previous partitioning n favors that of the current solution n+1. In order to achieve this, we identified a global k-means operator and we do “what if” analysis in the aspect of the objectives to see the better initialization in case the number of clusters is increased by one. After evaluating all, a feedback mechanism is supplied at the back-end to analyze the partitioning results with different indices. The entire system has been tested with a real world dataset: glass. The reported results demonstrate the applicability and effectiveness of the proposed approach.