Sliding-Window Based Method to Discover High Utility Patterns from Data Streams

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)


High utility pattern mining is one of the emerging researches in data mining. Mining these patterns from the evolving data streams is a big challenge, due the characteristics of data streams like high arrival rate, unbounded and gigantic in size, etc. Commonly there are three window models (landmark window, sliding window, time fading window) used in data streams. However, in most applications, users are interested in recent happenings. Hence, sliding window model has attracted high interest among three. Many approaches have been proposed based on the sliding window model. However, most of the approaches are based on level-wise candidate generation and text approach. In view of this, we propose an efficient one pass, tree based approach for mining high utility patterns over data streams. Experimental results show that the performance of our approach is better than the level-wise approach.


Data streams Sliding window High utility pattern Frequent pattern mining Data mining 


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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Indian School of MinesDhanbadIndia

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