Mining Weighted Frequent Patterns Using Adaptive Weights
Existing weighted frequent pattern (WFP) mining algorithms assume that each item has fixed weight. But in our real world scenarios the weight (price or significance) of an item can vary with time. Reflecting such change of weight of an item is very necessary in several mining applications such as retail market data analysis and web click stream analysis. In this paper, we introduce a novel concept of adaptive weight for each item and propose an algorithm AWFPM (adaptive weighted frequent pattern mining). Our algorithm can handle the situation where the weight (price or significance) of an item may vary with time. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using adaptive weights.
KeywordsData mining knowledge discovery weighted frequent pattern mining adaptive weight
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