A fuzzy k-modes algorithm for clustering categorical data
A fuzzy k-modes algorithm for clustering categorical data
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DOI：
10.1109/91.784206
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发表时间：
1999-08-01
影响因子：
11.9
通讯作者：
Ng, MK
中科院分区：
文献类型：
--
作者：
Huang, ZX;Ng, MK
这种对应描述了对模糊k-均值算法的扩展，用于聚类分类数据。通过对分类对象和模式使用简单的匹配不相似性度量而不是聚类的均值，开发了一种新的方法，该方法允许使用k-均值范式来有效地聚类大型分类数据集。提出了一种模糊k模式算法，并用实验结果验证了该算法的有效性。
This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.