模糊c-means算法与ckMeans算法的比较研究

Rogeario R. de Vargas, B. R. C. Bedregal
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引用次数: 17

摘要

聚类在数据挖掘、图像分割和其他模式识别问题中是一种有用的方法。当需要聚类的对象或模式较多时,模糊聚类过程会比较慢。本文讨论了一种算法ckMeans,它能够减少必须聚类的不同模式的数量,而不会对分区质量产生不利影响。通过计算一个新的数学方程来获得中心聚类。为了验证所提出的方法,我们将原始模糊c均值算法与本文提出的算法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparative study between fuzzy c-means and ckMeans algorithms
Clustering is a useful approach in data mining, image segmentation, and other problems of pattern recognition. Fuzzy clustering process can be quite slow when there are many objects or pattern to be clustered. This article discusses about an algorithm, ckMeans, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting partition quality. The reduction is done by calculating a new mathematical equation to obtaining center cluster. To validate the proposed methodology we compared the original fuzzy c-means algorithm with that proposed in this paper.
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