Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.
Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.
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DOI：
10.1155/2014/564829
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发表时间：
2014
影响因子：
--
通讯作者：
Zhuang Y
中科院分区：
文献类型：
--
作者：
Fong S;Deb S;Yang XS;Zhuang Y
传统的 K 均值聚类算法的缺点是陷入局部最优，而局部最优取决于初始质心的随机值。优化算法的优点是引导迭代计算寻找全局最优，同时避免局部最优。这些算法通过在多个搜索代理的作用下尽早收敛到全局最优值来帮助加速聚类过程。受大自然的启发，一些当代的优化算法，包括 Ant、Bat、Cuckoo、Firefly 和 Wolf 搜索算法，模仿蜂群行为，使它们能够在合理的时间内合作转向最佳目标。众所周知，这些所谓的自然启发优化算法都有各自的特点，在不同的应用中也各有利弊。当这些算法与 K 均值聚类机制相结合，通过避免局部最优和寻找全局最优来提高聚类质量时，新的混合算法有望产生前所未有的性能。在本文中，我们报告了将自然启发的优化方法集成到 K 均值算法中的评估实验结果。除了评价聚类质量的标准评价指标外，以自然启发的优化方法为基础的扩展K均值算法也被应用于图像分割，作为应用场景的案例研究。
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.
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影响因子：
10
作者：
Senthilnath, J.;Omkar, S. N.;Mani, V.
通讯作者：
Mani, V.
DOI：
10.1109/3477.484436
发表时间：
1996-02-01
影响因子：
--
作者：
Dorigo, M;Maniezzo, V;Colorni, A
通讯作者：
Colorni, A
DOI：
10.1016/b978-0-12-405163-8.00018-1
发表时间：
2013-01-01
期刊：
SWARM INTELLIGENCE AND BIO-INSPIRED COMPUTATION: THEORY AND APPLICATIONS
影响因子：
--
作者：
Fong, Simon
通讯作者：
Fong, Simon
DOI：
10.1109/tpami.1984.4767478
发表时间：
1984-01-01
影响因子：
23.6
作者：
SELIM, SZ;ISMAIL, MA
通讯作者：
ISMAIL, MA