ERAPN, an Algorithm for Extraction Positive and Negative Association Rules in Big Data

Bemarisika, Parfait; Thewis, André

doi:10.1007/978-3-319-98539-8_25

Cited by 8 publications

(11 citation statements)

References 10 publications

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“…On the other hand, as we pointed out above, it is immediate that for all itemsets 𝑋 and 𝑌 such that 𝑋 ⊂ 𝑌 , we have 𝑀 𝐺𝐾 (𝑋 → 𝑌 ) < 𝑀 𝐺𝐾 (𝑌 → 𝑋 ) [20]. Now, for all 𝑋 and 𝑌 , we have 𝑀 𝐺𝐾 (𝑌 → 𝑋 ) = 𝑀 𝐺𝐾 (𝑋 → 𝑌 ) [20]. Therefore, 𝑀 𝐺𝐾 (𝑋 → 𝑌 ) < 𝑀 𝐺𝐾 (𝑋 → 𝑌 ), i.e.…”

Section: Generating Valid Association Rulesmentioning

confidence: 72%

“…We then seek, for each subset, a member subset which can call on orginal members. Indeed, we demonstrated that 𝑀 𝐺𝐾 (𝑋 → 𝑌 ) = −𝑀 𝐺𝐾 (𝑋 → 𝑌 ), ∀𝑋 , 𝑌 ⊆ [20]. As result, if 𝑋 → 𝑌 is interesting, then 𝑋 → 𝑌 cannot be interesting.…”

Section: Generating Valid Association Rulesmentioning

confidence: 72%

“…Very often, the response time of mining rules is not always better when the database is dense. For this, we adopt a pruning reduce-rules-space procedure [20,21]. The challenge of this procedure is to reduce the number of rules without loss of information.…”

Section: Generating Valid Association Rulesmentioning

confidence: 99%

“…Soient 𝑋 1 → 𝑌 \𝑋 1 et 𝑋 2 → 𝑌 \𝑋 2 deux régles quelconques telles que 𝑋 1 ⊆ 𝑋 2 ⊆ 𝑌 , on a 𝑀 𝐺𝐾 (𝑋 1 → 𝑌 \𝑋 1 ) ⩽ 𝑀 𝐺𝐾 (𝑋 2 → 𝑌 \𝑋 2 ), i.e. 𝑚𝑔𝑘(𝑋 1 , 𝑌 ) ⩽ 𝑚𝑔𝑘(𝑋 2 , 𝑌 ) [20].…”

Section: Generating Valid Association Rulesunclassified

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CONCISE: An Algorithm for Mining Positive and Negative Non-Redundant Association Rules

Bemarisika¹,

Thewis²

2021

SCRS Conference Proceedings on Intelligent Systems

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One challenge problem in association rules mining is the huge size of the extracted rule set many of which are uninteresting and redundant. In this paper, we propose an efficient algorithm CONCISE for generating all non-redundant positive and negative association rules. We first introduce an algorithm CMG (Closed, Maximal and Generators) for mining all frequent closed, maximal and their generators itemsets from large transaction databases. We then define four new bases representing non-redundant association rules from these frequent itemsets. We prove that these bases significantly reduce the number of extracted rules. We show the efficiency of our algorithm by computational experiments compared with existing algorithms.

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Section: Generating Valid Association Rulesmentioning

confidence: 72%

Section: Generating Valid Association Rulesmentioning

confidence: 72%

Section: Generating Valid Association Rulesmentioning

confidence: 99%

Section: Generating Valid Association Rulesunclassified

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CONCISE: An Algorithm for Mining Positive and Negative Non-Redundant Association Rules

Bemarisika¹,

Thewis²

2021

SCRS Conference Proceedings on Intelligent Systems

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“…ARM is an evolving research area and various algorithms have been developed for the genera-tion of strong interesting association rules (ARs) in large datasets. The prominent algorithms in-clude the positive AR (Agrawal et al, 1993;Bar-alis et al, 2008;Krishnapuram, 2016), negative AR (Balakrishna et al, 2019;Jabbour et al, 2018;Kong et al, 2018), and combined approach for neg-ative and positive rules for large datasets (Bagui & Dhar, 2018;Bemarisika & Totohasina, 2018;Par-fait et al, 2018;Zhao et al, 2017). Moreover, ap-plication specific ARM algorithms have been de-veloped for various areas including medicine (Bo-rah & Nath, 2018;Harahap et al, 2018;Moses et al, 2015), crime (Buczak & Gifford, 2010;Has-sani et al, 2016), agriculture (Bhatia & Gupta, 2014;Bisht & Samantaray, 2015;Geetha, 2015), distributed environments (Qin et al, 2016;Salah et al, 2017), data warehousing (Usman, 2017) etc.…”

Section: Related Workmentioning

confidence: 99%

Mining predicate rules without minimum support threshold

Ahmad

Sim

Shah

et al. 2021

KJS publishes peer-review articles in Mathematics, Computer Sci

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Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence.

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Elimination of Redundant Association Rules

Bemarisika

Thewis

2019

Advances in Intelligent Systems and Computing

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ERAPN, an Algorithm for Extraction Positive and Negative Association Rules in Big Data

Cited by 8 publications

References 10 publications

CONCISE: An Algorithm for Mining Positive and Negative Non-Redundant Association Rules

CONCISE: An Algorithm for Mining Positive and Negative Non-Redundant Association Rules

Mining predicate rules without minimum support threshold

Elimination of Redundant Association Rules

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