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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zaifei Liao Xinjie Lu Tian Yang Hongan Wang |
| Copyright Year | 2009 |
| 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. |
| Starting Page | 133 |
| Ending Page | 137 |
| File Size | 357345 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769537351 |
| DOI | 10.1109/FSKD.2009.407 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-14 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Knowledge engineering Data analysis Uncertainty fuzzy set Data engineering Data mining sliding window missing data data quality k-means clustering Databases Clustering algorithms Signal processing algorithms Fuzzy set theory Fuzzy systems |
| Content Type | Text |
| Resource Type | Article |
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