A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data

Jinchao Ji; Wei Pang; Xiao Han; Chunguang  Zhou; Zhe Wang

doi:10.1016/j.knosys.2012.01.006

A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data

Jinchao Ji, Wei Pang, Xiao Han, Chunguang Zhou, Zhe Wang

Jilin University

Research output: Contribution to journal › Article › peer-review

140 Citations (Scopus)

Abstract

In many applications, data objects are described by both numeric and categorical features. The k-prototype algorithm is one of the most important algorithms for clustering this type of data. However, this method performs hard partition, which may lead to misclassification for the data objects in the boundaries of regions, and the dissimilarity measure only uses the user-given parameter for adjusting the significance of attribute. In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co-occurrence of values to evaluate the dissimilarity between data objects and prototypes of clusters. This measure also takes into account the significance of different attributes towards the clustering process. Then we present our algorithm for clustering mixed data. Finally, the performance of the proposed method is demonstrated by a series of experiments on four real world datasets in comparison with that of traditional clustering algorithms.

Original language	English
Pages (from-to)	129-135
Number of pages	7
Journal	Knowledge-Based Systems
Volume	30
Early online date	21 Jan 2012
DOIs	https://doi.org/10.1016/j.knosys.2012.01.006
Publication status	Published - Jun 2012

Bibliographical note

A paid open access option is available for this journal.
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Keywords

fuzzy clustering
data mining
mixed data
dissimilarity measure
attribute significance

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10.1016/j.knosys.2012.01.006Licence: Unspecified

Cite this

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abstract = "In many applications, data objects are described by both numeric and categorical features. The k-prototype algorithm is one of the most important algorithms for clustering this type of data. However, this method performs hard partition, which may lead to misclassification for the data objects in the boundaries of regions, and the dissimilarity measure only uses the user-given parameter for adjusting the significance of attribute. In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co-occurrence of values to evaluate the dissimilarity between data objects and prototypes of clusters. This measure also takes into account the significance of different attributes towards the clustering process. Then we present our algorithm for clustering mixed data. Finally, the performance of the proposed method is demonstrated by a series of experiments on four real world datasets in comparison with that of traditional clustering algorithms.",

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N1 - A paid open access option is available for this journal. Voluntary deposit by author of pre-print allowed on Institutions open scholarly website and pre-print servers Voluntary deposit by author of authors post-print allowed on institutions open scholarly website including Institutional Repository Deposit due to Funding Body, Institutional and Governmental mandate only allowed where separate agreement between repository and publisher exists Set statement to accompany deposit Published source must be acknowledged Must link to journal home page or articles' DOI Publisher's version/PDF cannot be used Articles in some journals can be made Open Access on payment of additional charge NIH Authors articles will be submitted to PubMed Central after 12 months Authors who are required to deposit in subject-based repositories may also use Sponsorship Option

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AB - In many applications, data objects are described by both numeric and categorical features. The k-prototype algorithm is one of the most important algorithms for clustering this type of data. However, this method performs hard partition, which may lead to misclassification for the data objects in the boundaries of regions, and the dissimilarity measure only uses the user-given parameter for adjusting the significance of attribute. In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co-occurrence of values to evaluate the dissimilarity between data objects and prototypes of clusters. This measure also takes into account the significance of different attributes towards the clustering process. Then we present our algorithm for clustering mixed data. Finally, the performance of the proposed method is demonstrated by a series of experiments on four real world datasets in comparison with that of traditional clustering algorithms.

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JO - Knowledge-Based Systems

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ER -

A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data

Abstract

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Keywords

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