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Author: search.filters.author.Tseng, Vincent S.Subject: search.filters.subject.Utility mining

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Now showing 1 - 4 of 4
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    Mining Minimal High-Utility Itemsets
    (2016)
    Fournier-Viger, Philippe
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    Lin, Jerry Chun-Wei
    ;
    Wu, Cheng-Wei
    ;
    Tseng, Vincent S.
    ;
    Faghihi, Usef
    Mining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset mining algorithms is that they can return a large number of HUIs. Analyzing a large result set can be very time-consuming for users. To address this issue, concise representations of high-utility itemsets have been proposed such as closed HUIs, maximal HUIs and generators of HUIs. In this paper, we explore a novel representation called the minimal high utility itemsets (MinHUIs), defined as the smallest sets of items that generate a high profit, study its properties, and design an efficient algorithm named MinFHM to discover it. An extensive experimental study with real-life datasets shows that mining MinHUIs can be much faster than mining other concise representations or all HUIs, and that it can greatly reduce the size of the result set presented to the user.
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    Item type:Item,
    Efficient Algorithms for Mining Top-K High Utility Itemsets
    (2016-01-01)
    Tseng, Vincent S.
    ;
    Wu, Cheng-Wei
    ;
    Fournier-Viger, Philippe
    ;
    Yu, Philip S.
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    資訊工程學系
    High utility itemsets (HUIs) mining is an emerging topic in data mining, which refers to discovering all itemsets having a utility meeting a user-specified minimum utility threshold min_util. However, setting min_util appropriately 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_util is set too low, too many HUIs will be generated, which may cause the mining process to be very inefficient. On the other hand, if min_util is set too high, it is likely that no HUIs will 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 (mining Top-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. Empirical evaluations on both real and synthetic datasets show that the performance of the proposed algorithms is close to that of the optimal case of state-of-the-art utility mining algorithms.
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    Item type:Item,
    MINING TEMPORAL RARE UTILITY ITEMSETS IN LARGE DATABASES USING RELATIVE UTILITY THRESHOLDS
    (2008-11-01)
    Chu, Chun-Jung
    ;
    Tseng, Vincent S.
    ;
    Liang, Tyne
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Utility itemsets are considered to be the different values of individual items such as utilities, and utility mining and aims at identifying the itemsets with highest utilities. The temporal significant rare utility itemsets are those itemsets which appear infrequently in the current time window of large databases but are highly associated with specific data. In this paper, we propose two novel algorithms, namely TP-RUI (Two-Phase Rare Utility Itemsets) -Mine and TRUI (Temporal Rare Utility Itemsets) -Mine, for mining temporal rare utility itemsets from temporal databases. To the best of our knowledge, this is the first work on mining temporal rare utility itemsets from temporal databases. The novel contribution of TRUI-Mine is particularly that it can effectively identify the temporal rare utility itemsets by generating fewer temporal high transaction-weighted utilization 2-itemsets in temporal databases. In this way, the process under all time windows of temporal databases can be achieved effectively with limited memory space, less candidate itemsets and CPU I/O time. The experimental results show that TRUI-Mine can discover the temporal rare utility itemsets with higher performance and less candidate itemsets compared to the other algorithm TP-RUI-Mine that is also proposed in this paper by us under various experimental conditions.
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    Item type:Item,
    An efficient algorithm for mining high utility itemsets with negative item values in large databases
    (2009-09-15)
    Chu, Chun-Jung
    ;
    Tseng, Vincent S.
    ;
    Liang, Tyne
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Utility itemsets typically consist of items with different values such as utilities, and the aim of utility mining is to identify the itemsets with highest utilities. In the past studies on utility mining, the values of utility itemsets were considered as positive. In some applications, however, an itemset may be associated with negative item values. Hence, discovery of high utility itemsets with negative item values is important for mining interesting patterns like association rules. In this paper, we propose a novel method, namely HUINIV (High Utility Itemsets with Negative Item Values)-Mine, for efficiently and effectively mining high utility itemsets from large databases with consideration of negative item values. To the best of our knowledge, this is the first work that considers the concept of negative item values in utility mining. The novel contribution of HUINIV-Mine is that it can effectively identify high utility itemsets by generating fewer high transaction-weighted utilization itemsets such that the execution time can be reduced substantially in mining the high utility itemsets. In this way, the process of discovering all high utility itemsets with consideration of negative item values can be accomplished effectively with less requirements on memory space and CPU I/O. This meets the critical requirements of temporal and spatial efficiency for mining high utility itemsets with negative item values. Through experimental evaluation, it is shown that HUINIV-Mine outperforms other methods substantially by generating much less candidate itemsets under different experimental conditions. (C) 2009 Elsevier Inc. All rights reserved.