Abstract
Article history: Received October 15, 2013 Received in revised format March 6 2014 Accepted March 24, 2014 Available online March 26 2014 Clustering plays an essential role for data analysis and it has been widely used in different fields like data mining, machine learning and pattern recognition. Clustering problem divides some data, which is more similar to each other in terms of parameters under consideration. One of available methods about this area is k-means algorithm. Despite dependency of this algorithm on initial condition and convergence to local optimal points, it classifies n data to k clusters with high speed. Since we encounter a huge volume of data in clustering problems, one of suitable methods for optimal clustering is to use a meta-heuristic algorithm, which improves clustering operation. In this paper, differential evolution algorithm is utilized for solving available problems in k-means algorithm. In this paper, meta-heuristic algorithm has been used for solving data clustering problems. The applied algorithm has been compared with k-means algorithm on six known dataset from UCI database. Results show that this algorithm achieves better clustering than k-means algorithm. © 2014 Growing Science Ltd. All rights reserved.
Highlights
Data clustering is one of the most complicated engineering problems and it is considered as an NPhard problem
One of available methods about this area is k-means algorithm. Despite dependency of this algorithm on initial condition and convergence to local optimal points, it classifies n data to k clusters with high speed
Differential evolution algorithm is utilized for solving available problems in k-means algorithm
Summary
Data clustering is one of the most complicated engineering problems and it is considered as an NPhard problem. Clustering algorithms are generally classified as hierarchical clustering and partition clustering (Han et al, 2006; Frigui & Krishnapuram, 1999). Hierarchical clustering normally groups data objects with a sequence of partitions, using singleton clusters either to a cluster including all individuals or vice versa. Hierarchical procedures can be either agglomerative or divisive: agglomerative algorithms start with each element as a separate cluster and merge them in successively larger clusters. Divisive algorithms begin with the whole set and divides it into successively smaller clusters (Jain et al, 1999; Rokach & Maimon, 2005). Partition procedures that we concerned in this paper, try to divide the data set into a set of
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