Rare association rule mining from incremental databases

Borah, Anindita; Nath, Bhabesh

doi:10.1007/s10044-018-0759-3

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…Setting

, and

, we used the proposed TSARM-UDP method and LTARMalgorithm [ 33 , 35 ] to discover knowledge from the BF data. Finally, twenty-five rules and 11 rules were obtained, respectively.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…Recent work about graph association rule mining [ 32 ] has the potential to take temporal information into account. In [ 33 ], to resolve the issue of incremental rare association rule mining, Borah et al presented a single-pass tree-based approach for extracting rare association rules when new data were inserted into the original database. The approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database.…”

Section: Introductionmentioning

confidence: 99%

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TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns

Zhao

et al. 2021

Entropy

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In many industrial domains, there is a significant interest in obtaining temporal relationships among multiple variables in time-series data, given that such relationships play an auxiliary role in decision making. However, when transactions occur frequently only for a period of time, it is difficult for a traditional time-series association rules mining algorithm (TSARM) to identify this kind of relationship. In this paper, we propose a new TSARM framework and a novel algorithm named TSARM-UDP. A TSARM mining framework is used to mine time-series association rules (TSARs) and an up-to-date pattern (UDP) is applied to discover rare patterns that only appear in a period of time. Based on the up-to-date pattern mining, the proposed TSAR-UDP method could extract temporal relationship rules with better generality. The rules can be widely used in the process industry, the stock market, etc. Experiments are then performed on the public stock data and real blast furnace data to verify the effectiveness of the proposed algorithm. We compare our algorithm with three state-of-the-art algorithms, and the experimental results show that our algorithm can provide greater efficiency and interpretability in TSARs and that it has good prospects.

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“…Setting

, and

, we used the proposed TSARM-UDP method and LTARMalgorithm [ 33 , 35 ] to discover knowledge from the BF data. Finally, twenty-five rules and 11 rules were obtained, respectively.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns

Zhao

et al. 2021

Entropy

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“…However, extraction of rare patterns or itemsets have recently gained much importance considering its manifold applications in several domains [10]. Substantial quantity of research has already been performed in the area of rare pattern mining that contributes enormous techniques looking for these previously unwanted rare itemsets [9,[12][13][14]16,17]. The rare pattern mining techniques considered for comparative analysis have been described below.…”

Section: Rare Itemset Generationmentioning

confidence: 99%

“…Several techniques have been proposed in the literature taking into account the issue of incremental rare pattern generation. Borah and Nath [13] developed an incremental rare pattern mining technique for earthquake trend analysis and anticipation. The technique could only handle the case of transaction insertion.…”

Section: Rare Itemset Generationmentioning

confidence: 99%

Comparative evaluation of pattern mining techniques: an empirical study

Borah

Nath

2020

Complex Intell. Syst.

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Pattern mining has emerged as a compelling field of data mining over the years. Literature has bestowed ample endeavors in this field of research ranging from frequent pattern mining to rare pattern mining. A precise and impartial analysis of the existing pattern mining techniques has therefore become essential to widen the scope of data analysis using the notion of pattern mining. This paper is therefore an attempt to provide a comparative scrutiny of the fundamental algorithms in the field of pattern mining through performance analysis based on several decisive parameters. The paper provides a structural classification of the widely referenced techniques in four pattern mining categories: frequent, maximal frequent, closed frequent and rare. It provides an analytical comparison of these techniques based on computational time and memory consumption using benchmark real and synthetic data sets. The results illustrate that tree based approaches perform exceptionally well over level wise approaches in case of dense data sets for all the categories. However, for sparse data sets, level wise approaches performed better than the former ones. This study has been carried out with an aim to analyze the pros and cons of the well known pattern mining techniques under different categories. Through this empirical study, an endeavor has been made to enable the researchers identify some fruitful and promising research directions in one of the most remarkable area of research, pattern mining.

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“…The paradigm of frequent pattern mining considers the rare patterns to be of least importance, demanding their removal during the phase of pattern generation. Of late, it has been identified that the rare patterns are significant for many application domains [7,8,[10][11][12]. A pattern is considered to be rare if its support value lies below the pre-defined support value.…”

Section: Literature Reviewmentioning

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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Rare association rule mining from incremental databases

Cited by 14 publications

References 25 publications

TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns

TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns

Comparative evaluation of pattern mining techniques: an empirical study

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

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