title: Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.
abstract: Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.
authors.affiliation: Department of Computer and Information Science, University of Macau, Macau.
authors.forename: Simon
authors.identifier: 0000-0002-1848-7246
authors.initials: S
authors.lastname: Fong
authors.affiliation: Department of Computer Science and Engineering, Cambridge Institute of Technology, Ranchi 835103, India.
authors.forename: Suash
authors.initials: S
authors.lastname: Deb
authors.affiliation: School of Science and Technology, Middlesex University, The Burroughs, London NW4 4BT, UK.
authors.forename: Xin-She
authors.initials: XS
authors.lastname: Yang
authors.affiliation: Department of Computer and Information Science, University of Macau, Macau.
authors.forename: Yan
authors.identifier: 0000-0002-5449-024X
authors.initials: Y
authors.lastname: Zhuang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008962:Models, Theoretical