Multi-level Fuzzy Association Rules Mining via Determining Minimum Supports and Membership Functions

Mahmoudi, Ehsan Vejdani; Sabetnia, Elahe; Torshiz, Masood Niazi; Jalali, Mehrdad; Tabrizi, Ghamarnaz Tadayon

doi:10.1109/isms.2011.20

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…Vejdani et al proposed a method that extracted multilevel membership functions by Ant Colony Systems algorithm without specifying the actual minimum support [14]. To enhance the efficiency of computing, Mahmoudi et al optimized Vejdani's method by fixing the functions for each item followed by computing minimum supports [15]. Wang et al took advantages of the OLAP and data mining technology in multilevel association rules mining which brought efficiency and flexibility [16].…”

Section: State Of the Artmentioning

confidence: 99%

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

Zeng

Liu

2014

Mathematical Problems in Engineering

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Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold.

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Section: State Of the Artmentioning

confidence: 99%

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

Zeng

Liu

2014

Mathematical Problems in Engineering

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“…Classifications of related elements can be thought of as hierarchical trees, pre-mapped for real-world uses. Inner nodes define classes or concepts that are created from lower-level nodes; [4] the leaf nodes of the hierarchical tree represent the real elements that the transactions are searching for. Fig.…”

Section: Introductionmentioning

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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“…Related item taxonomies are normally predefined in real-world purposes and can be interpreted as hierarchy trees. Terminal nodes on the trees express actual items looking in transactions; internal nodes describe classes or concepts built from lower-level nodes [7]. A simple example is given in Fig.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

Darwish¹,

Amer²,

Taktak³

2016

ijarai

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Abstract-Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the userspecified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.

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Multi-level Fuzzy Association Rules Mining via Determining Minimum Supports and Membership Functions

Cited by 8 publications

References 15 publications

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

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

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

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