Mining Negatives Association Rules Using Constraints

Jabbour, Saïd; Mazouri, Fatima Zahra El; Saïs, Lakhdar

doi:10.1016/j.procs.2018.01.146

Cited by 23 publications

(12 citation statements)

References 14 publications

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“…7 Jabbour et al [44] 2018 To mine the most relevant negative rules, the mining task of negative association rules is regularly combined with new measure, for example, lift or conviction to restrict the arrangement of separated association rules have been suggested in this work. They have addressed the problem of mining strong negative rules by extending the previous SAT-based approach [45].…”

Section: Discussion and Analysismentioning

confidence: 99%

A review and analysis on knowledge discovery and data mining techniques

Singh¹,

Dubey²

2018

IJATEE

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Section: Discussion and Analysismentioning

confidence: 99%

A review and analysis on knowledge discovery and data mining techniques

Singh¹,

Dubey²

2018

IJATEE

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“…The Apriori algorithm is a method to extract frequent items from the health transaction and find rules in which the association among independent other items meets the minimum support [5,6]. The minimum support set through repetitive performance evaluation is used for knowledge mining to find meaningful knowledge [21,22]. Figure 3 shows the discovery process of association rules using Apriori algorithm.…”

Section: B Discovery Of Association Relation Using Knowledge Miningmentioning

confidence: 99%

Multi-Level Health Knowledge Mining Process in P2P Edge Network

Baek

Chung

2021

IEEE Access

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Chronic diseases are increasing due to westernized eating habits and everyday life changes, and healthcare and disease prevention should be managed based on constant interest. Users, who are not health professionals, have difficulty in obtaining accurate information related to healthcare due to noise problems such as subjective opinions, distorted information, and exaggerated information. There is a need for a method that enables users to obtain meaningful information for healthcare and disease prevention in real-time among the vast amounts of data collected through search. In this study, we propose a multi-level health knowledge mining process in a P2P edge network. The proposed method suggests a P2P edge network to solve the overload problem of P2P networking, the noise problem, and the security problem of cloud computing and mines the health knowledge through the mutual information according to the association rules. In addition, the results of health knowledge mining are visualized to propose a method by which users can easily receive relevant health information. As a result of the performance evaluation, the F-measure using recall and precision is 83%, 79%, 75%, 74%, and 73% of the support ratings of 10%, 20%, 30%, 40%, and 50% Was derived. Accordingly, it is possible to process and analyze healthcare-related information in realtime through a multi-level based health knowledge tree based on the association of data collected by P2P edge computing. In addition, by visualizing meaningful information to the user through the embedding network structure, it provides personalized information for intuitive understanding.

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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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Mining Negatives Association Rules Using Constraints

Cited by 23 publications

References 14 publications

A review and analysis on knowledge discovery and data mining techniques

A review and analysis on knowledge discovery and data mining techniques

Multi-Level Health Knowledge Mining Process in P2P Edge Network

Mining predicate rules without minimum support threshold

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