一种新的验证索引，用于确定数据集中簇的数量

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222) Pub Date : 2001-07-15 DOI:10.1109/IJCNN.2001.938445

Hao-jun Sun, Shengrui Wang, Q. Jiang

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引用次数: 10

摘要

聚类分析在解决大型数据库数据挖掘等领域的实际问题中发挥着重要作用。在本文中，我们感兴趣的是基于模糊c均值(FCM)的算法。主要目的是设计一个有效的有效性函数来衡量聚类结果，并在实际应用中检测给定数据集的最佳聚类数。在回顾了相关文献后，我们提出了新的效度函数。实验结果和对比说明了新有效性函数的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A new validation index for determining the number of clusters in a data set

Clustering analysis plays an important role in solving practical problems in such domains as data mining in large databases. In this paper, we are interested in fuzzy c-means (FCM) based algorithms. The main purpose is to design an effective validity function to measure the result of clustering and detecting the best number of clusters for a given data set in practical applications. After a review of the relevant literature, we present the new validity function. Experimental results and comparisons will be given to illustrate the performance of the new validity function.

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IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)

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