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Real-time stream data miningbased onCanTree and Gtree

Real-time stream data miningbased onCanTree and Gtree

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 Real-time stream data miningbased onCanTree and Gtree

We face an increasing need to discover knowledge from data streams in real-time. Real-time stream data mining needs a compact data structure to store transactions in the recent sliding-window by one scan, and an efficient algorithm to discover frequent item sets from the compact data structure. In this paper, we propose a novel data mining algorithm, called Can Tree-G Tree, which discovers the complete frequent item sets from real-time transactions based on sliding-windows. The algorithm uses two data structures: Can Tree and G Tree. Can Tree compactly represents all transactions in a sliding-window by one scan, and serves as a base-tree. The algorithm efficiently maintains the base-tree by adding new trans-actions and removing old transactions without any reconstruction phases. A novel data structure, called G Tree (Group Tree), serves as a projection-tree for each data item. The algorithm traverses each node of the base-tree only once by using a top-down tree traver-sal method to build the projection-tree, and discovers frequent item sets by low process-ing cost. The proposed algorithm is therefore effective for discovering frequent item sets in real-time stream data. Our performance evaluation experiments with other algorithms based on CPS Tree and Can Tree-FP Tree show that our algorithm outperforms the other al-gorithms in the synthetic data set by about 35% and 26% of run-time cost, respectively. Also,we confirm that the proposed algorithm shows excellent results on real-world data sets


 


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  • Model: PROJ7557
  • 999 Units in Stock
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This product was added to our catalog on Monday 31 July, 2017.

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