Partitioning fuzzy c-means clustering algorithms for interval-valued data based on city-block distances

被引：4
|
作者：
de Carvalho, Francisco de A. T. [1 ]
Barbosa, Gibson B. N. [1 ]
Pimentel, Julio T. [2 ]
机构：
[1] Univ Fed Pernambuco UFPE, Ctr Informat CIn, Av Jornalista Anibal Fernandes S-N, BR-50740560 Recife, PE, Brazil
[2] Univ Fed Pernambuco UFPE, Dept Engenharia Mecanica, BR-50740560 Recife, PE, Brazil
来源：
2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS) | 2013年
关键词：
fuzzy c-means; interval-valued data; city-block distances;
D O I：
10.1109/BRACIS.2013.27
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents partitioning fuzzy c-means clustering algorithms for interval-valued data based on city-block distaces. These fuzzy c-means clustering algorithms give a fuzzy partition and a prototype for each fuzzy cluster by optimizing an adequacy criterion based on suitable adaptive and non-adaptive city-block distances between vectors of intervals. The adaptive city-block distances change at each algorithm iteration and are different from one fuzzy cluster to another. Experiments with real interval-valued data sets show the usefulness of these fuzzy clustering algorithms.
引用
收藏
页码：113 / 118
页数：6
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