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Subject: search.filters.subject.High-utility itemset

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Now showing 1 - 4 of 4
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    Item type:Item,
    Efficiently mining uncertain high-utility itemsets
    (2017-06-01)
    Lin, Jerry Chun-Wei
    ;
    Gan, Wensheng
    ;
    Fournier-Viger, Philippe
    ;
    Hong, Tzung-Pei
    ;
    Tseng, Vincent S.
    Data mining consists of deriving implicit, potentially meaningful and useful knowledge from databases such as information about the most profitable items. High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining. But most HUIM algorithms can only handle precise data, although big data collected in real-life applications using experimental measurements or noisy sensors is often uncertain. In this paper, an efficient algorithm, named Mining Uncertain High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utility itemsets (PHUIs) in uncertain data. Based on the probability-utility-list (PU-list) structure, the MUHUI algorithm directly mines PHUIs without generating candidates, and can avoid constructing PU-lists for numerous unpromising itemsets by applying several efficient pruning strategies, which greatly improve its performance. Extensive experiments conducted on both real-life and synthetic datasets show that the proposed algorithm significantly outperforms the state-of-the-art PHUI-List algorithm in terms of efficiency and scalability, and that the proposed MUHUI algorithm scales well when mining PHUIs in large-scale uncertain datasets.
  • Thumbnail Image
    Item type:Item,
    Efficient algorithms for mining high-utility itemsets in uncertain databases
    (2016-03-15)
    Lin, Jerry Chun-Wei
    ;
    Gan, Wensheng
    ;
    Fournier-Viger, Philippe
    ;
    Hong, Tzung-Pei
    ;
    Tseng, Vincent S.
    High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high utility itemsets (HUIs) assume that the information stored in databases is precise, i.e., that there is no uncertainty. But in many real-life applications, an item or itemset is not only present or absent in transactions but is also associated with an existence probability. This is especially the case for data collected experimentally or using noisy sensors. In the past, many algorithms were respectively proposed to effectively mine frequent itemsets in uncertain databases. But mining HUIs in an uncertain database has not yet been proposed, although uncertainty is commonly seen in real-world applications. In this paper, a novel framework, named potential high-utility itemset mining (PHUIM) in uncertain databases, is proposed to efficiently discover not only the itemsets with high utilities but also the itemsets with high existence probabilities in an uncertain database based on the tuple uncertainty model. The PHUI-UP algorithm (potential high-utility itemsets upper-bound-based mining algorithm) is first presented to mine potential high-utility itemsets (PHUIs) using a level-wise search. Since PHUI-UP adopts a generate-and test approach to mine PHUIs, it suffers from the problem of repeatedly scanning the database. To address this issue, a second algorithm named PHUI-List (potential high-utility itemsets PU-list-based mining algorithm) is also proposed. This latter directly mines PHUIs without generating candidates, thanks to a novel probability-utility-list (PU-list) structure, thus greatly improving the scalability of PHUI mining. Substantial experiments were conducted on both real-life and synthetic datasets to assess the performance of the two designed algorithms in terms of runtime, number of patterns, memory consumption, and scalability. (C) 2015 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Item type:Item,
    Efficient Mining of Uncertain Data for High-Utility Itemsets
    (2016)
    Lin, Jerry Chun-Wei
    ;
    Gan, Wensheng
    ;
    Fournier-Viger, Philippe
    ;
    Hong, Tzung-Pei
    ;
    Tseng, Vincent S.
    High-utility itemset mining (HUIM) is emerging as an important research topic in data mining. Most algorithms for HUIM can only handle precise data, however, uncertainty that are embedded in big data which collected from experimental measurements or noisy sensors in real-life applications. In this paper, an efficient algorithm, namely Mining Uncertain data for High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utility itemsets (PHUIs) from uncertain data. Based on the probability-utility-list (PU-list) structure, the MUHUI algorithm directly mine PHUIs without candidate generation and can reduce the construction of PU-lists for numerous unpromising itemsets by using several efficient pruning strategies, thus greatly improving the mining performance. Extensive experiments both on real-life and synthetic datasets proved that the proposed algorithm significantly outperforms the state-of-the-art PHUI-List algorithm in terms of efficiency and scalability, especially, the MUHUI algorithm scales well on large-scale uncertain datasets for mining PHUIs.
  • Thumbnail Image
    Item type:Item,
    EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets
    (2016)
    Fournier-Viger, Philippe
    ;
    Zida, Souleymane
    ;
    Lin, Jerry Chun-Wei
    ;
    Wu, Cheng-Wei
    ;
    Tseng, Vincent S.
    Discovering high-utility temsets in transaction databases is a popular data mining task. A limitation of traditional algorithms is that a huge amount of high-utility itemsets may be presented to the user. To provide a concise and lossless representation of results to the user, the concept of closed high-utility itemsets was proposed. However, mining closed high-utility itemsets is computationally expensive. To address this issue, we present a novel algorithm for discovering closed high-utility itemsets, named EFIM-Closed. This algorithm includes novel pruning strategies named closure jumping, forward closure checking and backward closure checking to prune non-closed high-utility itemsets. Furthermore, it also introduces novel utility upper-bounds and a transaction merging mechanism. Experimental results shows that EFIM-Closed can be more than an order of magnitude faster and consumes more than an order of magnitude less memory than the previous state-of-art CHUD algorithm.