NDLI: Fuzzy c-means clustering of partially missing data sets based on statistical representation

Content Provider	IEEE Xplore Digital Library
Author	Dan Li Chongquan Zhong Liyong Zhang
Copyright Year	2010
Description	Author affiliation: School of Electronic and Information Engineering, Dalian University of Technology, 116024, China (Dan Li; Chongquan Zhong; Liyong Zhang)
Abstract	The fuzzy c-means algorithm is a useful technique for clustering real s-dimensional data, but it can not be directly used for partially missing data sets. In this paper, the problem of missing data handling for fuzzy clustering is considered, and a statistical representation of missing attributes is proposed. The approach reduces the statistical analysis of missing attributes to the subsets of the dataset with similar data of incomplete data to impute the missing attributes, thus is helpful in enhancing the learning of missing attributes and the performance of fuzzy clustering based on the recovered data. Comparisons and analysis of the clustering results of the incomplete IRIS data demonstrate that the proposed statistical representation can estimate missing attributes rationally and improve the fuzzy c-means clustering of incomplete data.
Starting Page	460
Ending Page	464
File Size	268168
Page Count	5
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569767
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Iris Statistical analysis Clustering algorithms Prototypes clustering Partitioning algorithms statistical representation fuzzy c-means incomplete data Nearest neighbor searches
Content Type	Text
Resource Type	Article

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