missing data imputation: a fuzzy k-means clustering algorithm over sliding window

	missing data imputation: a fuzzy k-means clustering algorithm over sliding window
	Liao Zaifei; Lu Xinjie; Yang Tian; Wang Hongan
	2009
会议名称	6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
会议录名称	6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
页码	133-137
会议日期	August 14,
会议地点	Tianjin, China
收录类别	其他
出版地	United States
出版者	United States
ISBN	9780769537351
部门归属	(1) Intelligence Engineering Lab., Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China
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.
关键词	Cluster Analysis
主办者	Tianjin University of Technology
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/8482
专题	人机交互技术与智能信息处理实验室
推荐引用方式 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.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
missing data imputat（348KB）			开放获取	--	请求全文