Improving K-means Clustering Using Evolutionary Algorithms

Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining, and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. In this paper a clustering algorithm is proposed based on the standard K-Means clustering and Evolutionary algorithms. Experiments with 1 bench-mark datasets have shown similar or slightly better quality of the results compared to standard KMeansalgorithm and other algorithm. The experiment results show that proposed algorithm clustering has not only higher accuracy but also higher level of stability. And the faster convergence speed can also be validated by statistical results

کلیدواژه ها:

data clustering ، K-Means ، Evolutionary Algorithms