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

Mansouri, T; Ravasan, AZ; Gholamian, MR

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.

Citation

Mansouri, T., Ravasan, A., & Gholamian, M. (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), 1. https://doi.org/10.4018/ijdwm.2014070101

• Journal Article Type: Article
• Publication Date: Jul 1, 2014
• Deposit Date: Jun 9, 2021
• Journal: International Journal of Data Warehousing and Mining
• Print ISSN: 1548-3924
• Electronic ISSN: 1548-3932
• Publisher: IGI Global
• Volume: 10
• Issue: 3
• Pages: 1
• DOI: https://doi.org/10.4018/ijdwm.2014070101
• Publisher URL: https://doi.org/10.4018/ijdwm.2014070101
• Related Public URLs: http://www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=1085