missing data imputation: a fuzzy k-means clustering algorithm over sliding window
文献类型：会议论文
| 作者 | Liao Zaifei ; Lu Xinjie ; Yang Tian ; Wang Hongan |
| 出版日期 | 2009 |
| 会议名称 | 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 |
| 会议日期 | August 14, |
| 会议地点 | Tianjin, China |
| 关键词 | Cluster analysis |
| 页码 | 133-137 |
| 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. |
| 收录类别 | 其他 |
| 会议主办者 | Tianjin University of Technology |
| 会议录 | 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
 |
| 会议录出版者 | United States |
| 会议录出版地 | United States |
| 语种 | 英语 |
| ISBN号 | 9780769537351 |
| 源URL | [http://124.16.136.157/handle/311060/8482]  |
| 专题 | 软件研究所_人机交互技术与智能信息处理实验室_会议论文 |
| 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]. 见:6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009. Tianjin, China. August 14,. |
入库方式： OAI收割
来源：软件研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。