Missing data imputation: a fuzzy K-means clustering algorithm over sliding window

Authors:
Zaifei Liao;Xinjie Lu;Tian Yang;Hongan Wang
Affiliations:
Intelligence Engineering Lab., Institute of Software Chinese Academy of Sciences, Beijing, China;Intelligence Engineering Lab., Institute of Software Chinese Academy of Sciences, Beijing, China;Intelligence Engineering Lab., Institute of Software Chinese Academy of Sciences, Beijing, China;Intelligence Engineering Lab., Institute of Software Chinese Academy of Sciences, Beijing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Year:
2009

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Abstract

Fuzzy set theory is motivated by the practical needs to manage and process uncertainty inherent in real world problem solving. It is useful in applications to data mining, conflict analysis, and so on. Although ignored by much of the related work, the high rate and unbounded nature of data make the sliding window indispensable. In this paper, we present a fuzzy k-means clustering algorithm over sliding window for the missing value imputation of incomplete data to improve the data quality. The experiments show that our missing data imputation algorithm tends to be more tolerant of imprecision and uncertainty and can lead to a better performance with accuracy guarantees.