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Efficient Algorithms for Mining Top-K High Utility Itemsets

Efficient Algorithms for Mining Top-K High Utility Itemsets

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 Efficient Algorithms for Mining Top-K High Utility Itemsets

High utility itemsets ( HUIs) mining is an emerging topic in data mining, which refers to discovering all itemsets having a utility meeting a user-specifiedminimum utility threshold min_util. However, setting min_utilappropriately is a difficult problem for users. Generally speaking, finding an appropriate minimum utility threshold by trial and error is a tedious process for users. If min_utilis set too low, too manyHUIswill be generated, which may cause the mining process to be very inefficient. On the other hand, ifmin_util is set too high, it is likely that no HUIswill be found. In this paper, we address the above issues by proposing a new framework for top- k high utility itemset mining , where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU ( miningTop-K Utility itemsets) and TKO( mining Top-K utility itemsets in One phase) are proposed for mining such itemsets without the need to set min_util. We provide a structural comparison of the two algorithms with discussions on their advantages and limitations. Empiricalevaluations on both real and synthetic datasets show that the performance of the proposed algorithms is close to that of the optimalcase of state-of-the-art utility mining algorithms.


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  • Model: PROJ7509
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Friday 28 July, 2017.