Efficiently Mining Top-K High Utility Sequential Patterns

Yin, Junfu; Zheng, Zhedong; Cao, Longbing; Song, Yin; Wei, Wei

doi:10.1109/icdm.2013.148

Cited by 78 publications

(38 citation statements)

References 10 publications

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“…Recently, there have been some emerging studies on mining top-k high utility itemsets [Wu et al 2012], Concise and Lossless Representation of High Utility Itemsets [Wu et al 2011;Wu et al 2014] and High Utility Sequential Patterns Yin et al 2012;Yin et al 2013]. The adoption or extension of PUN-list to mine these special high utility patterns is an interesting topic for future research.…”

Section: Resultsmentioning

confidence: 99%

An efficient structure for fast mining high utility itemsets

Deng

2018

Appl Intell

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High utility itemset mining has emerged to be an important research issue in data mining since it has a wide range of real life applications. Although a number of algorithms have been proposed in recent years, there seems to be still a lack of efficient algorithms since these algorithms suffer from either the problem of low efficiency of calculating candidates' utilities or the problem of generating huge number of candidates. In this paper, we propose a novel data structure called PUNlist (PU-tree-Node list), which maintains both the utility information about an itemset and utility upper bound for facilitating the processing of mining high utility itemsets. Based on PUN-lists, we present a method, called MIP (Mining high utility Itemset using PUN-Lists), for fast mining high utility itemsets. The efficiency of MIP is achieved with three techniques. First, itemsets are represented by a highly condensed data structure, PUN-list, which avoids costly, repeatedly utility computation. Second, the utility of an itemset can be efficiently calculated by scanning the PUN-list of the itemset and the PUN-lists of long itemsets can be fast constructed by the PUN-lists of short itemsets. Third, by employing the utility upper bound lying in the PUN-lists as the pruning strategy, MIP directly discovers high utility itemsets from the search space, called set-enumeration tree, without generating numerous candidates. Extensive experiments on various synthetic and real datasets show that PUN-list is very effective since MIP is at least an order of magnitude faster than recently reported state-of-the-art algorithms on average.

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Section: Resultsmentioning

confidence: 99%

An efficient structure for fast mining high utility itemsets

Deng

2018

Appl Intell

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“…Several algorithms have been proposed to find top-k utility itemsets in transactions [17], sequence databases [18] and data streams [12]. Most top-k algorithms use a common search strategy for finding the results.…”

Section: Related Workmentioning

confidence: 99%

UP-Hist Tree

Dawar

Goyal

2014

Proceedings of the 19th International Database Engineering &Amp; Applications Symposium on - IDEAS '15

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High-utility itemset mining is an emerging research area in the field of Data Mining. Several algorithms were proposed to find high-utility itemsets from transaction databases and use a data structure called UP-tree for their working. However, algorithms based on UP-tree generate a lot of candidates due to limited information availability in UP-tree for computing utility value estimates of itemsets. In this paper, we present a data structure named UP-Hist tree which maintains a histogram of item quantities with each node of the tree. The histogram allows computation of better utility estimates for effective pruning of the search space. Extensive experiments on real as well as synthetic datasets show that our algorithm based on UP-Hist tree outperforms the state of the art pattern-growth based algorithms in terms of the total number of candidate high utility itemsets generated that needs to be verified.

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“…Recently, some interesting issues of HUSPM have been extensively studied that can improve the effectiveness of mining high-utility sequential patterns. For example, the problems of mining top-k high-utility sequential patterns [45,41], discovering periodic HUSPs [10], mining HUSPs with multiple minimum utility thresholds [28], and incrementally mining HUSPs on a dynamic database [40] have been addressed. It should be noted that several genetic algorithms have been developed for HUIM (e.g., HUIM-BPSO [27] and HUIM-ACS [43]), but they have not been proposed to deal with HUSPM yet.…”

Section: Utility-driven Mining On Sequencesmentioning

confidence: 99%

ProUM: Projection-based utility mining on sequence data

Gan

Lin

Zhang

et al. 2020

Information Sciences

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Utility is an important concept in economics. A variety of applications consider utility in real-life situations, which has lead to the emergence of utility-oriented mining (also called utility mining) in the recent decade. Utility mining has attracted a great amount of attention, but most of the existing studies have been developed to deal with itemset-based data. Time-ordered sequence data is more commonly seen in real-world situations, which is different from itemset-based data. Since they are time-consuming and require large amount of memory usage, current utility mining algorithms still have limitations when dealing with sequence data. In addition, the mining efficiency of utility mining on sequence data still needs to be improved, especially for long sequences or when there is a low minimum utility threshold. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store the necessary information of the sequence-order and utility. ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption. Furthermore, a new upper bound named sequence extension utility is proposed and several pruning strategies are further applied to improve the efficiency of ProUM. By taking utility theory into account, the derived high-utility sequential patterns have more insightful and interesting information than other kinds of patterns. Experimental results showed that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms in terms of execution time, memory usage, and scalability.

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Efficiently Mining Top-K High Utility Sequential Patterns

Cited by 78 publications

References 10 publications

An efficient structure for fast mining high utility itemsets

An efficient structure for fast mining high utility itemsets

UP-Hist Tree

ProUM: Projection-based utility mining on sequence data

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