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题名:
missing data imputation: a fuzzy k-means clustering algorithm over sliding window
作者: Liao Zaifei ; Lu Xinjie ; Yang Tian ; Wang Hongan
会议文集: 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
会议名称: 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
会议日期: August 14,
出版日期: 2009
会议地点: Tianjin, China
关键词: Cluster analysis
出版者: United States
出版地: United States
收录类别: 其他
ISBN: 9780769537351
部门归属: (1) Intelligence Engineering Lab., Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China
主办者: Tianjin University of Technology
英文摘要: 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.
语种: 英语
内容类型: 会议论文
URI标识: http://ir.iscas.ac.cn/handle/311060/8482
Appears in Collections:人机交互技术与智能信息处理实验室_会议论文

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Recommended Citation:
Liao Zaifei,Lu Xinjie,Yang Tian,et al. missing data imputation: a fuzzy k-means clustering algorithm over sliding window[C]. 见:6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009. Tianjin, China. August 14,.
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