A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering

Taha Mansouri, Ahad Zare Ravasan and Mohammad Reza Gholamian
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Taha Mansouri: Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran
Ahad Zare Ravasan: Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran
Mohammad Reza Gholamian: School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

International Journal of Data Warehousing and Mining (IJDWM), 2014, vol. 10, issue 3, 1-14

Abstract: One of the most widely used algorithms to solve clustering problems is the K-means. Despite of the algorithm's timely performance to find a fairly good solution, it shows some drawbacks like its dependence on initial conditions and trapping in local minima. This paper proposes a novel hybrid algorithm, comprised of K-means and a variation operator inspired by mutation in evolutionary algorithms, called Noisy K-means Algorithm (NKA). Previous research used K-means as one of the genetic operators in Genetic Algorithms. However, the proposed NKA is a kind of individual based algorithm that combines advantages of both K-means and mutation. As a result, proposed NKA algorithm has the advantage of faster convergence time, while escaping from local optima. In this algorithm, a probability function is utilized which adaptively tunes the rate of mutation. Furthermore, a special mutation operator is used to guide the search process according to the algorithm performance. Finally, the proposed algorithm is compared with the classical K-means, SOM Neural Network, Tabu Search and Genetic Algorithm in a given set of data. Simulation results statistically demonstrate that NKA out-performs all others and it is prominently prone to real time clustering.

Date: 2014
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