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

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

Taha Mansouri, Ahad Zare Ravasan, Mohammad Reza Gholamian
Copyright: © 2014 |Volume: 10 |Issue: 3 |Pages: 14
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781466653771|DOI: 10.4018/ijdwm.2014070101
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MLA

Mansouri, Taha, et al. "A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering." IJDWM vol.10, no.3 2014: pp.1-14. http://doi.org/10.4018/ijdwm.2014070101

APA

Mansouri, T., Ravasan, A. Z., & Gholamian, M. R. (2014). A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering. International Journal of Data Warehousing and Mining (IJDWM), 10(3), 1-14. http://doi.org/10.4018/ijdwm.2014070101

Chicago

Mansouri, Taha, Ahad Zare Ravasan, and Mohammad Reza Gholamian. "A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering," International Journal of Data Warehousing and Mining (IJDWM) 10, no.3: 1-14. http://doi.org/10.4018/ijdwm.2014070101

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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.

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