Mamdani Fuzzy Inference System for Rating the Performance of Sponge City Programme

Wang, Chen; Li, Yang; Du, Junbao; Corzo, Gerald

doi:10.1109/icsai57119.2022.10005410

Cited by 1 publication

(1 citation statement)

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“…Only one modification of the Apriori algorithm, which deals with datasets within databases, particularly transaction records, or records including a certain number of fields, or which uses a multi-level -bottom-up approach‖ has been made to ML_T2L1 [21] [30]. Which runs the ML_T2L1 algorithm from the transaction table and these tables contain the hierarchical information in which it is encoded.…”

Section: A Research Contributionmentioning

confidence: 99%

Enhancing Customer Relationship Management Using Fuzzy Association Rules and the Evolutionary Genetic Algorithm

Dahab¹,

Haggag²,

Fotouh³

2023

IJACSA

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The importance of Customer Relationship Management (CRM) has never been higher. Thus, companies are forced to adopt new strategies to focus on customers, given the competitive climate in which they operate. Also, companies have been able to maintain customer data within large databases that contain all information related to customers, thanks to the tremendous technological development seen recently. Multilevel quantitative association mining is a significant field for achieving motivational associations between data components with multiple abstraction levels. This paper develops a methodology to support CRM to improve the relationship between retail companies and their customers in the retail sector to retain existing customers and attract more new customers, by applying data mining techniques using the genetic algorithm through which an integrated search is performed. The proposed model can be implemented because the proposed model does not need the minimum levels of support and trust required by the user, and it has been confirmed that the algorithm proposed in this research can powerfully create non-redundant fuzzy multi-level association rules, according to the results of these experiments.

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Section: A Research Contributionmentioning

confidence: 99%