Mining Frequent Itemsets with Weights over Data Stream Using Inverted Matrix
Long Nguyen Hung, Thuy Nguyen Thi Thu · International Journal of Information Technology and Computer Science · 2016
In recent years, the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds.In this paper, we have proposed an algorithm called M FIWDSIM for mining frequent itemsets with weights over a data stream using Inverted Matrix [10].The main idea is moving data stream to an inverted matrix saved in the computer disks so that the algorithms can mine on it many times with different support thresholds as well as alternative minimu m weights.Moreover, this inverted matrix can be accessed to mine in different times for user's requirements without recalculation.By analy zing and evaluating, the MFIWDSIM can be seen as the better algorith m co mpared to WSWFP-stream [9] for min ing frequent itemsets with weights over data stream.