On Fuzzy Clustering for Incomplete Spherical Data and for Incomplete Multivariate Categorical Data

We propose simultaneously applying homogeneity analysis and fuzzy clustering that simultaneously partitions individuals and items in categorical multivariate datasets. This objective function includes two types of memberships. One is conventional membership representing the degree of membership of each individual in each cluster. The other is an additional parameter that represents typicality of item. A numerical experiment demonstrates that our proposal is useful in quantifying categorical data, taking the typicality of each item into account.

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Categorical data analysis using multiobjective differential evolution based fuzzy clustering

2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI) ◽

10.1109/icacci.2013.6637491 ◽

2013 ◽

Author(s):

Indrajit Saha ◽

Debasree Maity ◽

Ujjwal Maulik

Keyword(s):

Data Analysis ◽

Differential Evolution ◽

Fuzzy Clustering ◽

Categorical Data ◽

Categorical Data Analysis

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Improved Fuzzy Clustering Techniques for Categorical Data

10.1063/1.3078143 ◽

2009 ◽

Author(s):

Indrajit Saha ◽

Ujjwal Maulik ◽

Sio-Iong Ao ◽

Alan Hoi-Shou Chan ◽

Hideki Katagiri ◽

...

Keyword(s):

Fuzzy Clustering ◽

Categorical Data ◽

Clustering Techniques

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Correspondence analysis of multivariate categorical data by weighted least-squares

Biometrika ◽

10.1093/biomet/75.3.457 ◽

1988 ◽

Vol 75 (3) ◽

pp. 457-467 ◽

Cited By ~ 69

Author(s):

MICHAEL J. GREENACRE

Keyword(s):

Least Squares ◽

Correspondence Analysis ◽

Categorical Data ◽

Weighted Least Squares ◽

Multivariate Categorical

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Performance Comparison of Collaborative Filtering Using Fuzzy Clustering for Spherical Data

2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS) ◽

10.1109/scis-isis.2018.00108 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tadafumi Kondo ◽

Yuchi Kanzawa

Keyword(s):

Collaborative Filtering ◽

Fuzzy Clustering ◽

Performance Comparison ◽

Spherical Data

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A novel fuzzy clustering algorithm with between-cluster information for categorical data

Fuzzy Sets and Systems ◽

10.1016/j.fss.2012.06.005 ◽

2013 ◽

Vol 215 ◽

pp. 55-73 ◽

Cited By ~ 26

Author(s):

Liang Bai ◽

Jiye Liang ◽

Chuangyin Dang ◽

Fuyuan Cao

Keyword(s):

Fuzzy Clustering ◽

Categorical Data ◽

Clustering Algorithm ◽

Fuzzy Clustering Algorithm

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Fuzzy Clustering Ensemble Algorithm for Partitioning Categorical Data

2009 International Conference on Business Intelligence and Financial Engineering ◽

10.1109/bife.2009.48 ◽

2009 ◽

Cited By ~ 2

Author(s):

Taoying Li ◽

Yan Chen

Keyword(s):

Fuzzy Clustering ◽

Categorical Data ◽

Clustering Ensemble ◽

Ensemble Algorithm ◽

Fuzzy Clustering Ensemble

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Nonparametric Bayes Modeling of Multivariate Categorical Data

Journal of the American Statistical Association ◽

10.1198/jasa.2009.tm08439 ◽

2009 ◽

Vol 104 (487) ◽

pp. 1042-1051 ◽

Cited By ~ 76

Author(s):

David B. Dunson ◽

Chuanhua Xing

Keyword(s):

Categorical Data ◽

Nonparametric Bayes ◽

Multivariate Categorical

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MGLM: An R Package for Multivariate Categorical Data Analysis

The R Journal ◽

10.32614/rj-2018-015 ◽

2018 ◽

Vol 10 (1) ◽

pp. 73 ◽

Cited By ~ 1

Author(s):

Juhyun Kim ◽

Yiwen Zhang ◽

Joshua Day ◽

Hua Zhou

Keyword(s):

Data Analysis ◽

Categorical Data ◽

R Package ◽

Categorical Data Analysis ◽

Multivariate Categorical

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FCM-Type Fuzzy Clustering of Mixed Databases Considering Nominal Variable Quantification

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2007.p0162 ◽

2007 ◽

Vol 11 (2) ◽

pp. 162-167 ◽

Cited By ~ 4

Author(s):

Katsuhiro Honda ◽

◽

Ryo Uesugi ◽

Hidetomo Ichihashi

Keyword(s):

Objective Function ◽

Linear Combination ◽

Fuzzy Clustering ◽

Categorical Data ◽

Clustering Algorithm ◽

Cluster Structure ◽

Cost Functions ◽

Fuzzy Partition ◽

Clustering Criterion ◽

The Relationship

This paper proposes a clustering algorithm that performs FCM-type clustering of datasets including categorical data. The proposed algorithm iterates categorical data quantification in FCE clustering so that quantified scores suit the current fuzzy partition. The objective function is the linear combination of two cost functions, i.e., the objective function of FCE clustering and the clustering criterion of quantified category scores. Because quantified category scores are assigned considering the relationship among categories, they are useful for interpreting the cluster structure.

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