A data clustering approach based on universal gravity rule. (October 2015)

• Record Type: Journal Article
• Authors: Bahrololoum, Abbas; Nezamabadi-pour, Hossein; Saryazdi, Saeid
• Is Part Of: Engineering applications of artificial intelligence. Volume 45(2015:Sep.)
• Page Start: 415
• Page End: 428
• Publication Date: 2015-10
• Subjects: Data clustering -- Law of gravity -- Nature inspired algorithm -- Clustering analysis -- Data classification; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.07.018
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper, a new robust data clustering algorithm inspired by Newtonian law of gravity is proposed. The proposed algorithm not only reduces the effects of noise and outliers but also, it is not sensible to the initial positions of the centroids. In the proposed method, data points and the cluster centroids are considered as fixed celestial objects and movable objects, respectively. The celestial objects apply a gravity force to the movable objects and change their positions in the feature space and therefore, the best positions of the cluster centroids are determined by employing the law of gravity. To evaluate the performance of the proposed algorithm, a comparative experimental study with some well-known clustering algorithms, using three visual datasets as well as several benchmark datasets from UCI, is performed. The experimental results confirm the effectiveness and the efficiency of the proposed clustering algorithm.