Efficient Mining of Weighted Frequent Patterns over Data Streams
作者： Chowdhury Farhan Ahmed , Syed Khairuzzaman Tanbeer , Byeong-Soo Jeong
DOI: 10.1109/HPCC.2009.36
关键词: Data mining 、 Data stream mining 、 Knowledge engineering 、 Computer science 、 Sliding window protocol 、 Knowledge extraction 、 Data stream 、 Information engineering
摘要: By considering different weights of the items, weighted frequent pattern (WFP)mining can discover more important knowledge compared to traditional mining. Therefore, WFP mining becomes an research issue in data and discovery area. However, existing algorithms cannot be applied for stream because they require multiple database scans. Moreover, extract recent change a adaptively. In this paper, we propose sliding window based novel technique WFPMDS (Weighted Frequent Pattern Mining over Data Streams) using single scan form elements. Extensive performance analyses show that our is very efficient streams.
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