An Efficient Algorithm for Mining Closed Weighted Frequent Pattern over Data Streams
Weighted frequent pattern mining is suggested to discover more important frequent pattern by considering different weights of each item, and closed frequent pattern mining can reduces the number of frequent patterns and keep sufficient result information. In this paper, we propose an efficient algorithm DS_CWFP to mine closed weighted frequent pattern mining over data streams. We present an efficient algorithm based on sliding window and can discover closed weighted frequent pattern from the recent data. A new efficient DS_CWFP data structure is used to dynamically maintain the information of transactions and also maintain the closed weighted frequent patterns has been found in the current sliding window. Three optimization strategies are present. The detail of the algorithm DS_CWFP is also discussed. Experimental studies are performed to evaluate the good effectiveness of DS_CWFP.
closed weighted frequent pattern mining data streams DSCWFP data mining Algorithm optimization Sliding window
Wang Jie Zeng Yu
School of Management, Capital Normal University Beijing 100089, China Beijing Computing Center Beijing 100094, China
国际会议
北京
英文
153-156
2012-06-22（万方平台首次上网日期，不代表论文的发表时间）