Exploring Fuzzy Ontologies in Mining Generalized Association Rules

Ayres, Rodrigo Moura Juvenil; Ribeiro, Marcela Xavier; Santos, Marilde Terezinha Prado

doi:10.1007/978-3-642-31137-6_51

Cited by 1 publication

(4 citation statements)

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“…Reference [9] proposes a new algorithm called Context FOntGAR for mining generalized association rules under all levels of fuzzy ontologies. The algorithm addresses the problem of redundancy and includes a treatment for grouping rules.…”

Section: Figure 1: Impact Of Support Value On the Number Of Generated...mentioning

confidence: 99%

“…-Introduces rule schemas and operators for pruning and filtering rules. [9] Context FOntGAR algorithm -Addresses redundancy in generalized association rules.…”

Section: Figure 1: Impact Of Support Value On the Number Of Generated...mentioning

confidence: 99%

“…Post-processing of association rules can be approached using either traditional descriptive techniques or machine learning predictive techniques. References [5][6][7][8][9] have applied descriptive techniques, including merging, pruning, filtering, and more. Another option for post-processing is utilizing machine learning possibilities through descriptive techniques, as suggested by researchers such as [10,12,13].…”

Section: Thape: Post-processing Approachmentioning

confidence: 99%

“…However, it is important to note that filtering methods may inadvertently eliminate important rules or introduce bias into the rule set, depending on the criteria used for selection. Summarization with Ontologies [6][7][8][9] and Pruning with Meta-rules [10,11] are other descriptive analysis techniques utilized in association rule learning. Summarization with Ontologies involves reducing the number of rules that need to be explored by iteratively incorporating the user's knowledge during the post-processing stage.…”

Section: Introductionmentioning

confidence: 99%

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THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Alawadh,

Barnawi

2024

CMC

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Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant or redundant rules, uncovering interesting and useful rules, exploring hidden association rules that may affect other factors, and providing backtracking ability for a given product. The proposed approach offers a tailored method that suits specific goals for retailers, enabling them to gain a better understanding of customer behavior based on factual transactions in the target market. We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness. Through this application, we successfully mined a concise set of highly interesting and useful association rules. Out of the 11,265 rules generated, we identified 125 rules that are particularly relevant to the business context. These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

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Section: Figure 1: Impact Of Support Value On the Number Of Generated...mentioning

confidence: 99%

“…-Introduces rule schemas and operators for pruning and filtering rules. [9] Context FOntGAR algorithm -Addresses redundancy in generalized association rules.…”

Section: Figure 1: Impact Of Support Value On the Number Of Generated...mentioning

confidence: 99%