A study on a fuzzy clustering for mixed numerical and categorical incomplete data

被引:0
作者
Furukawa, Takashi [1 ]
Ohnishi, Shin-ichi [2 ]
Yamanoi, Takahiro [2 ]
机构
[1] Hokkai Gakuen Univ, Grad Sch Engn, Sapporo, Hokkaido, Japan
[2] Hokkai Gakuen Univ, Fac Engn, Sapporo, Hokkaido, Japan
来源
2013 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY 2013) | 2013年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most clustering methods focus on numerical data. However, most data existing in databases are both categorical and numerical. To date, clustering methods have been developed to analyze only complete data. Although we sometimes encounter data sets that contain one or more missing feature values (incomplete data), traditional clustering methods cannot be used for such data. Thus, we study this theme and discuss clustering methods that can handle mixed numerical and categorical incomplete data. In this paper, we propose an algorithm that uses the missing categorical data imputation method and distances between numerical data that contain missing values. Furthermore, we apply fuzzy clustering for interpreting results that are vague.
引用
收藏
页码:425 / 428
页数:4
相关论文
共 8 条
[1]  
[Anonymous], Pattern Recognition with Fuzzy Objective Function Algorithms
[2]  
Dunn J. C., 1973, Journal of Cybernetics, V3, P32, DOI 10.1080/01969727308546046
[3]   Fuzzy c-means clustering of incomplete data [J].
Hathaway, RJ ;
Bezdek, JC .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2001, 31 (05) :735-744
[4]  
Honda K, 2004, CYB INT SYST 2004 IE, V1
[5]   Extensions to the k-means algorithm for clustering large data sets with categorical values [J].
Huang, ZX .
DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 2 (03) :283-304
[6]  
Naija Yosr, 2008, IEEE INT C DAT MIN W
[7]   Alternative c-means clustering algorithms [J].
Wu, KL ;
Yang, MS .
PATTERN RECOGNITION, 2002, 35 (10) :2267-2278
[8]  
Wu Sen, 2012, IEEE INT C SYST MAN