Mining High Utility Sequential Patterns with Negative Item Values

Xu, Tiantian; Xiang, Dong; Xu, Jie; Dong, Xue

doi:10.1142/s0218001417500355

Cited by 21 publications

(5 citation statements)

References 13 publications

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“…In the future, we will consider introducing the concept of actionable combined mining into high utility negative sequential pattern mining [31,[41][42][43][44] to discover combined negative sequential patterns, which can offer more useful information for decision-making.…”

Section: Discussionmentioning

confidence: 99%

Mining actionable combined high utility incremental and associated sequential patterns

Shi

Gong

et al. 2023

PLoS ONE

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High utility sequential pattern (HUSP) mining aims to mine actionable patterns with high utilities, widely applied in real-world learning scenarios such as market basket analysis, scenic route planning and click-stream analysis. The existing HUSP mining algorithms mainly attempt to improve computation efficiency while maintaining the algorithm stability in the setting of large-scale data. Although these methods have made some progress, they ignore the relationship between additional items and underlying sequences, which directly leads to the generation of redundant sequential patterns sharing the same underlying sequence. Hence, the mined patterns’ actionability is limited, which significantly compromises the performance of patterns in real-world applications. To address this problem, we present a new method named Combined Utility-Association Sequential Pattern Mining (CUASPM) by incorporating item/sequence relations, which can effectively remove redundant patterns and extract high discriminative and strongly associated sequential pattern combinations with high utilities. Specifically, we introduce the concept of actionable combined mining into HUSP mining for the first time and develop a novel tree structure to select discriminative high utility sequential patterns (HUSPs) for downstream tasks. Furthermore, two efficient strategies (i.e., global and local strategies) are presented to facilitate mining HUSPs while guaranteeing utility growth and high levels of association. Last, two parameters are introduced to evaluate the interestingness of patterns to choose the most useful actionable combined HUSPs (ACHUSPs). Extensive experimental results demonstrate that the proposed CUASPM outperforms the baselines in terms of execution time, memory usage, mining high discriminative and strongly associated HUSPs.

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Section: Discussionmentioning

confidence: 99%

Mining actionable combined high utility incremental and associated sequential patterns

Shi

Gong

et al. 2023

PLoS ONE

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“…The work in [7] uses a lexicographic quantitative sequence tree (LQS-tree) to extract the complete set of HUSP and two pruning methods are used to reduce the search space in the LQS-tree. The work in [8] considers the negative item value in HUSP mining and an algorithm named HUSPNIV is proposed to mine HUSP with a negative item value. The work in [9] proposes an efficient sequence-utility (SU)-chain structure to improve mining performance.…”

Section: High Utility Sequential Pattern Mining (Husp Mining)mentioning

confidence: 99%

“…Current research topics of utility-driven mining focus primarily on discovering patterns of high value (e.g., high profit) in large databases or analyzing the important factors (e.g., economic factors) in the data mining process [1]. High utility sequential pattern (HUSP) mining is an important task in utility-driven mining [2][3][4][5][6][7][8]. It focuses on extracting subsequences with a high utility (importance) from quantitative sequential databases.…”

Section: Introductionmentioning

confidence: 99%

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Zhang

et al. 2020

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As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.

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“…Zihayat et al [25] consider HUSPM approaches with data streams as input. Xu et al [26] propose the possibility for negative item values. Zida et al [27] propose solutions to the problem of identifying high utility sequential rules.…”

Section: Literature Reviewmentioning

confidence: 99%

Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists

Buffett

2020

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A ubiquitous challenge throughout all areas of data mining, particularly in the mining of frequent patterns in large databases, is centered on the necessity to reduce the time and space required to perform the search. The extent of this reduction proportionally facilitates the ability to identify patterns of interest. High utility sequential pattern mining (HUSPM) seeks to identify frequent patterns that are (1) sequential in nature and (2) hold a significant magnitude of utility in a sequence database, by considering the aspect of item value or importance. While traditional sequential pattern mining relies on the downward closure property to significantly reduce the required search space, with HUSPM, this property does not hold. To address this drawback, an approach is proposed that establishes a tight upper bound on the utility of future candidate sequential patterns by maintaining a list of items that are deemed potential candidates for concatenation. Such candidates are provably the only items that are ever needed for any extension of a given sequential pattern or its descendants in the search tree. This list is then exploited to significantly further tighten the upper bound on the utilities of descendent patterns. An extension of this work is then proposed that significantly reduces the computational cost of updating database utilities each time a candidate item is removed from the list, resulting in a massive reduction in the number of candidate sequential patterns that need to be generated in the search. Sequential pattern mining methods implementing these new techniques for bound reduction and further candidate list reduction are demonstrated via the introduction of the CRUSP and CRUSPPivot algorithms, respectively. Validation of the techniques was conducted on six public datasets. Tests show that use of the CRUSP algorithm results in a significant reduction in the overall number of candidate sequential patterns that need to be considered, and subsequently a significant reduction in run time, when compared to the current state of the art in bounding techniques. When employing the CRUSPPivot algorithm, the further reduction in the size of the search space was found to be dramatic, with the reduction in run time found to be dramatic to moderate, depending on the dataset. Demonstrating the practical significance of the work, experiments showed that time required for one particularly complex dataset was reduced from many hours to less than one minute.

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Mining High Utility Sequential Patterns with Negative Item Values

Cited by 21 publications

References 13 publications

Mining actionable combined high utility incremental and associated sequential patterns

Mining actionable combined high utility incremental and associated sequential patterns

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists

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