Mining maximal frequent patterns by considering weight conditions over data streams

Frequent pattern mining over data streams is currently one of the most interesting fields in data mining. Current databases have needed more immediate processes since enormous amounts of data are being accumulated and updated in real time. However, existing traditional approaches have not been entirely suitable for a data stream environment since they operate with more than two database scans. Moreover, frequent pattern mining over data streams mostly generates an enormous number of frequent patterns, thereby causing a significant amount of overheads. In addition, as weight conditions are very useful factors in reflecting importance for each object in the real world, it is necessary to apply them to the mining process in order to obtain more practical, meaningful patterns. To consider and solve these problems, we propose a novel method for mining Weighted Maximal Frequent Patterns (WMFPs) over data streams, called MWS (Maximal frequent pattern mining with Weight conditions over data Streams). MWS guarantees efficient mining performance in the data stream environment by scanning stream databases only once, and prevents overheads of pattern extractions with an abbreviated notation: a maximal frequent pattern form instead of the general one. Furthermore, MWS contributes to enhanced reliability of the mining results by applying weight conditions to each element of the data streams. Extensive experiments report that MWS has outstanding performance in comparison to previous algorithms.