MINING FREQUENT CLOSED PATTERNS WITH ITEM CONSTRAINTS IN DATA STREAMS
In order to efficiently filter the useful association rules through a large number of mined rules, some item constraints that are boolean expresssions are integrated into the associations discovery algorithm. The set of frequent closed patterns uniquely determines the complete set of all frequent patterns, and it can be orders of magnitude smaller than the latter. According to the features of data streams, a new algorithm, call DSCFCI, is proposed for mining frequent closed patterns with item constraints in data streams. The data stream is divided into a set of segments, and a new data structure called DSCFCI-tree is used to store the potential frequent closed patterns with item constraints dynamically. With the arrival of each batch of data, the algorithm builds a corresponding local DSCFCI-tree firstly, then updates and prunes the global DSCFCI-tree effectively to mine the frequent closed patterns with item constraints in the entire data stream. The experiments and analysis show that the algorithm has good performance.
Data mining data streams association rule frequent closed itemsets
WEI-CHENG HU BEN-NIAN WANG ZHUAN-LIU CHENG
Department of Computer Science, Tongling College, Tongling 244000, China Department of Computer Science, Tongling College, Tongling 244000, China College of Computer Science
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
274-280
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）