Towards missing data imputation: A study of fuzzy K-means clustering method

Dan Li; Jitender Deogun; William Spaulding; Bill Shuart

doi:10.1007/978-3-540-25929-9_70

Towards missing data imputation: A study of fuzzy K-means clustering method

Dan Li, Jitender Deogun, William Spaulding, Bill Shuart

Psychology

Research output: Contribution to journal › Conference article › peer-review

174 Scopus citations

Abstract

In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases (KDD), i.e. clustering technique. We combine the clustering method with soft computing, which tends to be more tolerant of imprecision and uncertainty, and apply a fuzzy clustering algorithm to deal with incomplete data. Our experiments show that the fuzzy imputation algorithm presents better performance than the basic clustering algorithm.

Original language	English (US)
Pages (from-to)	573-579
Number of pages	7
Journal	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	3066
DOIs	https://doi.org/10.1007/978-3-540-25929-9_70
State	Published - 2004
Event	4th International Conference, RSCTC 2004 - Uppsala, Sweden Duration: Jun 1 2004 → Jun 5 2004

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-25929-9_70

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abstract = "In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases (KDD), i.e. clustering technique. We combine the clustering method with soft computing, which tends to be more tolerant of imprecision and uncertainty, and apply a fuzzy clustering algorithm to deal with incomplete data. Our experiments show that the fuzzy imputation algorithm presents better performance than the basic clustering algorithm.",

author = "Dan Li and Jitender Deogun and William Spaulding and Bill Shuart",

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AU - Spaulding, William

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AB - In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases (KDD), i.e. clustering technique. We combine the clustering method with soft computing, which tends to be more tolerant of imprecision and uncertainty, and apply a fuzzy clustering algorithm to deal with incomplete data. Our experiments show that the fuzzy imputation algorithm presents better performance than the basic clustering algorithm.

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Towards missing data imputation: A study of fuzzy K-means clustering method

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