A novel hybrid algorithm based on K-means and evolutionary computations for real time clustering

Mansouri, T ORCID: https://orcid.org/0000-0003-1539-5546, Ravasan, AZ and Gholamian, MR 2014, 'A novel hybrid algorithm based on K-means and evolutionary computations for real time clustering' , International Journal of Data Warehousing and Mining, 10 (3) , p. 1.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: International Journal of Data Warehousing and Mining
Publisher: IGI Global
ISSN: 1548-3924
Related URLs:
Depositing User: T Mansouri
Date Deposited: 09 Jun 2021 09:32
Last Modified: 27 Aug 2021 21:54
URI: http://usir.salford.ac.uk/id/eprint/60890

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