文献类型：会议论文

英文题名：A Sliding-Window Approach for Finding Top-k Frequent Itemsets from Uncertain Streams

作者：Zhang, Xiaojian[1];Peng, Huili[2]

第一作者：张啸剑

通讯作者：Zhang, XJ[1]

机构：[1]Henan Univ Finance & Econ, Dept Comp Sci, Zhengzhou 450002, Peoples R China;[2]Henan Radio & Tel Univ, Dept Educ, Zhengzhou 450008, Peoples R China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Henan Univ Finance & Econ, Dept Comp Sci, Zhengzhou 450002, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

会议论文集：Joint International Conference on Asia-Pacific Web Conference (APWeb)/Web-Age Information Management (WAIM 2009)

会议日期：APR 02-04, 2008-2009

会议地点：Suzhou, PEOPLES R CHINA

语种：英文

外文关键词：Pattern recognition - Sensor networks

摘要：The analysis and management of uncertain data has attracted a lot of attention recently in many important applications such as pattern recognition and sensor network. Frequent itemset mining is often useful in analyzing uncertain data in those applications. However, previous works just focus on the static uncertain data instead of uncertain streams. In this paper, we Study the problem of mining top-k FIs in uncertain streams. We propose an efficient algorithm, called UTK-FI, based on sliding-window and Chemoff bound techniques for finding k most frequent iternsets of different sizes. Experimental results show that Our algorithm performs much better than many established methods in uncertain streams environment.