Performance Evaluation of Fuzzy Association Rule Mining Algorithms

Rahman, Tasnia; Kabir, Mir Md. Jahangir; Kabir, Monika

doi:10.1109/eict48899.2019.9068771

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Transactions come from a dataset that is broken into several sample data, with the execution speed of each algorithm application calculated. Eclat Algorithm was obtained with a speed of less than 1 second [19].…”

Section: Results and Analysis 31 Rule Algorithmmentioning

confidence: 99%

Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders

Putra,

Putri,

Sinaga

et al. 2024

PREDATECS

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Association rule is a data mining technique to find associative rules between a combination of items. This research aims to apply association rules algorithm in identifying popular topping combinations in food orders. This application aims to help restaurant owners or food businesses understand their customers' preferences and optimize their menu offerings. Data obtained from kaggle, the association rules algorithm is applied to this dataset to identify patterns or combinations of toppings that often appear together in orders. The results of this study show toppings with chocolate as a popular item in orders. These findings can provide valuable insights for food business owners in structuring their menus and determining attractive offers for customers. This study also applied a comparison between the apriori, fp- growth and eclat algorithms, with the result that the best item transaction rule was found: a combination of dill & unicorn toppings with chocolate with 60% confidence. Overall, the application of eclat algorithm in this study provides the best performance with higher execution speed, thus providing insight into customer preferences regarding topping combinations in food orders. Despite the shortcomings of the data form from this study, it is expected to help business owners in optimizing their offerings, increasing customer satisfaction, and improving their business performance.

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Section: Results and Analysis 31 Rule Algorithmmentioning

confidence: 99%

Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders

Putra,

Putri,

Sinaga

et al. 2024

PREDATECS

View full text Add to dashboard Cite

show abstract

“…The data processing algorithms in the Fuzzy law [1] are specified on large transaction datasets. The classic collection of features, the Fuzzy Optimization method, and the Modern Genetic Fuzzy Association rule DC automated framework was used to analyze performance.…”

Section: Literature Reviewmentioning

confidence: 99%

The Hash base Apriori Technique for Association Rule Mining and Data Sanitization

Jagtap

2022

SMSJ

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Under different circumstances, private information is exposed, and it must be sanitised before even being shared to address privacy issues. Data mining techniques can collect large amounts of data in a short amount of time. The information gathered by the powerful machine learning techniques may identify the most sensitive content, which pertains to an individual or organization. The degree of sensitivity of data belonging to a business or an agency might vary. Only approved individuals and organizations have access to this information. As a result, using access limitations to confirm the security of complicated data is not a complete operation. It can have an impact on the utility of a data mining solution, and the user may be able to re-identify sensitive data. To introduce instruments to find a mechanism for the security of confidential information. Finding ways to secure confidential data by developing data mining tools and procedures that can be applied to databases, despite the fact that this diminishes the data mining results' trust worthiness. We proposed a data sanitization strategy in this article that uses a frequent itemset classification approach with a modified apriori algorithm. The problem is to maintain intelligence information for vital arrangements while preventing the numerous exposures of company rule mining at the same time. Data sanitization strategy is used to perform a thorough investigation of numerous sequential pattern algorithms for ensuring the privacy of large amounts of data. Our research shows that our approach is efficient, scalable, and provides meaningful correction when compared to other methods used in existing systems. Keywords: Association Rule Mining, Classification, Frequent itemset, data privacy, data hiding, rule generation, support, confidence and transactional dataset

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“…Sets are technologies that provide us with accurate information that we may apply in a variety of fields, including business, engineering, and medical sciences. To find new knowledge, different strategies can be used to produce interesting rules known as association rule mining [2].…”

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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Performance Evaluation of Fuzzy Association Rule Mining Algorithms

Cited by 6 publications

References 9 publications

Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders

Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders

The Hash base Apriori Technique for Association Rule Mining and Data Sanitization

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

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