Automatic assignment of item weights for pattern mining on data streams
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Efficient mining of frequent items coupled with weight and /or support over progressive databases
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
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By considering different weights of the items, weighted frequent pattern (WFP)mining can discover more important knowledge compared to traditional frequent pattern mining. Therefore, WFP mining becomes an important research issue in data mining and knowledge discovery area. However, the existing algorithms cannot be applied for stream data mining because they require multiple database scans. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively. In this paper, we propose a sliding window based novel technique WFPMDS (Weighted Frequent Pattern Mining over Data Streams) using a single scan of data stream to discover important knowledge form the recent data elements. Extensive performance analyses show that our technique is very efficient for WFP mining over data streams.