Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms
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 h...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/564829 |
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| author | Simon Fong Suash Deb Xin-She Yang Yan Zhuang |
| author_facet | Simon Fong Suash Deb Xin-She Yang Yan Zhuang |
| author_sort | Simon Fong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1bc644d6d47a49e2a25ed7a237f9eb88 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-1bc644d6d47a49e2a25ed7a237f9eb882025-02-03T06:01:36ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/564829564829Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization AlgorithmsSimon Fong0Suash Deb1Xin-She Yang2Yan Zhuang3Department of Computer and Information Science, University of Macau, MacauDepartment of Computer Science and Engineering, Cambridge Institute of Technology, Ranchi 835103, IndiaSchool of Science and Technology, Middlesex University, The Burroughs, London NW4 4BT, UKDepartment of Computer and Information Science, University of Macau, MacauTraditional 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.http://dx.doi.org/10.1155/2014/564829 |
| spellingShingle | Simon Fong Suash Deb Xin-She Yang Yan Zhuang Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms The Scientific World Journal |
| title | Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms |
| title_full | Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms |
| title_fullStr | Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms |
| title_full_unstemmed | Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms |
| title_short | Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms |
| title_sort | towards enhancement of performance of k means clustering using nature inspired optimization algorithms |
| url | http://dx.doi.org/10.1155/2014/564829 |
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