ECLAT Algorithm for Frequent Item Set Generation with Association Rule Mining Algorithm

Bansal, Akanksha; Khare, Amit; Moriwal, Rahul

doi:10.32628/cseit206247

Cited by 2 publications

(1 citation statement)

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“…Various algorithms exist for mining frequent itemsets, such as Apriori [64] [65], AprioriTID [64] [65], Apriori Hybrid [64] [65], Eclat (Equivalence CLAss Transformation) [66], [67], and FP-growth (Frequent pattern) [64] [68]. In this paper, we utilize FP-growth algorithm to generate frequent itemsets since it has several advantages over other algorithms.…”

Section: B Frequent Pattern Miningmentioning

confidence: 99%

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Kannout,

Grzegorowski,

Grodzki

et al. 2024

IEEE Access

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Recommender systems (RS) are substantial for online shopping or digital content services. However, due to some data characteristics or insufficient historical data, may encounter considerable difficulties impacting the quality of their recommendations. This study introduces the clustering-based frequent pattern mining framework for recommender systems (Clustering-based FPRS) -a novel RS constituting several recommendation strategies leveraging agglomerative clustering and FP-growth algorithms. The developed strategies combine the generated frequent itemsets with collaborative-and content-filtering methods to address the cold-start problem, which occurs whenever a new user or item enters the system. In such cases, the RS has limited information about the new user or object. Thus, the recommendations may be inaccurate. The experimental evaluation on several benchmark datasets showed that Clustering-based FPRS is superior to state-of-the-art and could effectively alleviate the cold-start problem.INDEX TERMS Cold-start problem, recommender system, frequent pattern mining, clustering I. INTRODUCTION

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Section: B Frequent Pattern Miningmentioning

confidence: 99%

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Kannout,

Grzegorowski,

Grodzki

et al. 2024

IEEE Access

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Research on data mining model of fault operation and maintenance based on electric vehicle charging behavior

Zhu

et al. 2023

Front. Energy Res.

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In recent years, with the development of new energy technology and the country’s strong support for electric vehicles, there is a lack of effective electric vehicle charging fault analysis and diagnosis methods at this stage. A comprehensive analysis of the working principle of the charging process of electric vehicles, based on the clarification of the failure mechanism of the power battery and charging equipment, analyzes the fault-related factors affecting the power battery and charging equipment from multiple angles, and summarizes the relationship between the power battery and charging equipment. The feature parameters related to equipment failure are discretized by the k-means clustering algorithm. Using the optimized FP-Growth algorithm based on weights, the association rules between the power battery and charging equipment failures and the characteristic parameters of the failure factors are mined, and the correlation of the failures is analyzed based on the association rules, and the correlation between the failure factors and the failures is obtained relevant level.

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ECLAT Algorithm for Frequent Item Set Generation with Association Rule Mining Algorithm

Cited by 2 publications

References 8 publications

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Research on data mining model of fault operation and maintenance based on electric vehicle charging behavior

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