Mining Frequent Itemsets with Weights over Data Stream Using Inverted Matrix
Автор: Long Nguyen Hung, Thuy Nguyen Thi Thu
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 10 Vol. 8, 2016 года.
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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 MFIWDSIM 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 minimum weights. Moreover, this inverted matrix can be accessed to mine in different times for user's requirements without recalculation. By analyzing and evaluating, the MFIWDSIM can be seen as the better algorithm compared to WSWFP-stream [9] for mining frequent itemsets with weights over data stream.
Data mining, frequent itemset with weight, data stream, sliding window, inverted matrix
Короткий адрес: https://sciup.org/15012571
IDR: 15012571
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