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| missing data imputation: a fuzzy k-means clustering algorithm over sliding window | |
| Liao Zaifei; Lu Xinjie; Yang Tian; Wang Hongan | |
| 2009 | |
| Conference Name | 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 |
| Source | 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 |
| Pages | 133-137 |
| Conference Date | August 14, |
| Conference Place | Tianjin, China |
| Indexed Type | 其他 |
| Publish Place | United States |
| Publisher | United States |
| ISBN | 9780769537351 |
| Department | (1) Intelligence Engineering Lab., Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China |
| English 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 kmeans 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. © 2009 IEEE. |
| Keyword | Cluster Analysis |
| Sponsorship | Tianjin University of Technology |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/8482 |
| Collection | 人机交互技术与智能信息处理实验室 |
| Recommended Citation GB/T 7714 | Liao Zaifei,Lu Xinjie,Yang Tian,et al. missing data imputation: a fuzzy k-means clustering algorithm over sliding window[C]. United States:United States,2009:133-137. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| missing data imputat（348KB） | 开放获取 | -- | Application Full Text | |||
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