Subtractive Clustering for Categorical Data

被引：0
|
作者：
Gu, Lei [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
来源：
2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) | 2016年
关键词：
the categorical data; subtractive clustering method; k-modes; Hamming distance; MOUNTAIN METHOD; ALGORITHM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Previous subtractive clustering methods can be used for numerical data, but it cannot be applied to categorical data because attribute values of categorical data do not have a natural ordering. In this paper, one novel subtractive clustering method which is applied to some categorical data is given. The Euclidean distance is replaced by Hamming distance in this new approach. Some experiments in this paper are run on several UCI datasets, and some experimental results describe that this novel presented method can get the better clustering accuracies compared to traditional k-modes.
引用
收藏
页码：1229 / 1232
页数：4
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