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

Dan Li, Jitender Deogun, William Spaulding, Bill Shuart

Research output: Contribution to journalConference article

96 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)573-579
Number of pages7
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3066
StatePublished - Dec 9 2004
Event4th International Conference, RSCTC 2004 - Uppsala, Sweden
Duration: Jun 1 2004Jun 5 2004

Fingerprint

K-means Clustering
Imputation
Fuzzy Clustering
Missing Data
Clustering Methods
Clustering algorithms
Clustering Algorithm
Knowledge Discovery in Databases
Soft computing
Fuzzy Algorithm
Fuzzy clustering
Soft Computing
Imprecision
Incomplete Data
Data mining
Clustering
Tend
Uncertainty
Experiment
Experiments

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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