FP-Tree and Its Variants: Towards Solving the Pattern Mining Challenges

Borah, Anindita; Nath, Bhabesh

doi:10.1007/978-981-10-5828-8_51

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…Thus, CSPM is proposed to alleviate this problem (Bonchi and Lucchese, 2005 ; Nijssen and Zimmermann, 2014 ; Aoga et al, 2017 ). Constraint Programming and graphical representation of the sequence database have been shown to perform well for CSPM (Guns et al, 2017 ; Kemmar et al, 2017 ; Borah and Nath, 2018 ; Hosseininasab et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

Dichotomic Pattern Mining Integrated With Constraint Reasoning for Digital Behavior Analysis

Ghosh

Yadav

Wang

et al. 2022

Front. Artif. Intell.

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Sequential pattern mining remains a challenging task due to the large number of redundant candidate patterns and the exponential search space. In addition, further analysis is still required to map extracted patterns to different outcomes. In this paper, we introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes. Our approach takes advantage of constraint reasoning to find sequential patterns that occur frequently and exhibit desired properties. This allows the creation of novel pattern embeddings that are useful for knowledge extraction and predictive modeling. Based on dichotomic pattern mining, we present two real-world applications for customer intent prediction and intrusion detection. Overall, our approach plays an integrator role between semi-structured sequential data and machine learning models, improves the performance of the downstream task, and retains interpretability.

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Section: Related Workmentioning

confidence: 99%

Dichotomic Pattern Mining Integrated With Constraint Reasoning for Digital Behavior Analysis

Ghosh

Yadav

Wang

et al. 2022

Front. Artif. Intell.

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“…Graphical representations of a database have been shown to be effective in item-set mining (Han et al 2004;Pyun, Yun, and Ryu 2014;Borah and Nath 2018) and SPM (Masseglia, Poncelet, and Teisseire 2009). Previous works have also applied binary decision diagrams as a database modeling tool (Loekito and Bailey 2006;Loekito, Bailey, and Pei 2010;Cambazard, Hadzic, and O'Sullivan 2010), which are effective when the sequences of the database are similar, but typically do not scale otherwise.…”

Section: Related Workmentioning

confidence: 99%

Constraint-Based Sequential Pattern Mining with Decision Diagrams

Hosseininasab

Hoeve

Ciré

2019

AAAI

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Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.

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“…Database projection reduces the effort for itemset counting by restricting the size of the database used for support counting. The tree projection-based approaches are the most proficient one and have skillfully attempted to resolve the issues faced by the pattern mining community [9,13].…”

Section: Tree Projection-based Algorithmsmentioning

confidence: 99%

Tree based frequent and rare pattern mining techniques: a comprehensive structural and empirical analysis

Borah

Nath

2019

SN Appl. Sci.

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Pattern mining has emerged as a compelling field of research over the years due to its extensive popularity in several application domains. Researchers in this field have contributed ample endeavors, encompassing different issues encountered during pattern generation. The patterns are either extracted based on a level-wise exploration of the search space or by employing efficient tree data structures. Tree based approaches show remarkable performance over the former ones on various grounds and are therefore regarded as the most prominent techniques of pattern mining. A precise and impartial analysis of these eminent techniques is necessary to widen the scope of effective pattern mining using the notion of tree data structures. This paper is therefore an attempt to provide a comparative scrutiny of the fundamental tree based pattern mining techniques through performance analysis based on several decisive parameters. The paper provides a structural classification of the widely referenced tree based pattern mining techniques in four categories and an analytical comparison of these techniques using benchmark real and synthetic datasets. Through this empirical study, an endeavor has been made to enable the researchers identify the factors affecting the performance of the most well-known techniques of pattern mining: the tree based approaches.

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FP-Tree and Its Variants: Towards Solving the Pattern Mining Challenges

Cited by 10 publications

References 28 publications

Dichotomic Pattern Mining Integrated With Constraint Reasoning for Digital Behavior Analysis

Dichotomic Pattern Mining Integrated With Constraint Reasoning for Digital Behavior Analysis

Constraint-Based Sequential Pattern Mining with Decision Diagrams

Tree based frequent and rare pattern mining techniques: a comprehensive structural and empirical analysis

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