A Comparative Analysis of Apriori and FP-Growth Algorithms for Market Basket Analysis Using Multi-level Association Rule Mining

Alcan, Dilara; Ozdemir, Kubra; Ozkan, Berkay; Mucan, Ali Yigit; Özcan, Tuncay

doi:10.1007/978-3-031-25847-3_13

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Finally, the Apriori algorithm for ARM (association rule mining) was used to identify the association between comorbidities. The Apriori algorithm is widely used to analyze frequent item sets and association rules, as well as in the fields of finance, marketing, and healthcare [15][16][17][18][19].…”

Section: Discussionmentioning

confidence: 99%

Comorbidity Patterns and Management in Inpatients with Endocrine Diseases by Age Groups in South Korea: Nationwide Data

Kim,

Kim

2023

JPM

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This study aimed to examine comorbidity associations across age groups of inpatients with endocrine diseases as the primary diagnosis throughout the life cycle to develop an effective management strategy. Data were obtained from the Korean National Hospital Discharge In-depth Injury Survey (KNHDS) from 2006 to 2021, involving 68,515 discharged patients aged ≥ 19 years with a principal diagnosis of endocrine disease. A database was constructed for analysis, extracting general characteristics and comorbidities. Employing R version 4.2.3, the Chi-squared test and the Apriori algorithm of ARM (association rule mining) were used for analyzing general characteristics and comorbidity associations. There were more women (53.1%) than men (46.9%) (p < 0.001, with women (61.2 ± 17.2) having a higher average age than men (58.6 ± 58.6) (p < 0.001). Common comorbidities include unspecified diabetes mellitus; essential (primary) hypertension; unspecified diabetes mellitus; and other disorders of fluid, electrolyte, and acid-base balance. Notably, type 2 diabetes mellitus, disorders of lipoprotein metabolism and other lipidemia, polyneuropathy in diseases classified elsewhere, retinal disorders in diseases classified elsewhere, and essential (primary) hypertension prevail across all age groups. Association rules further highlight specific comorbidities appearing selectively in certain age groups. In conclusion, establishing a management strategy for comorbidities in patients with a primary diagnosis of an endocrine disorder is necessary.

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Section: Discussionmentioning

confidence: 99%

Comorbidity Patterns and Management in Inpatients with Endocrine Diseases by Age Groups in South Korea: Nationwide Data

Kim,

Kim

2023

JPM

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“…Scholars at home and abroad have achieved certain results in the research of rural revitalization featured industries' relevance based on association rule mining, which mainly applies association rule mining methods, such as Apriori algorithm [1] and Frequent Pattern-Growth (FP-Growth) algorithm, to find out the association laws between rural revitalization featured industries. Meanwhile, data collection and analysis techniques, including data mining, statistical analysis, and spatial analysis, are also combined to process and interpret rural industry data.…”

Section: Domestic and International Research Statusmentioning

confidence: 99%

“…The support of an association rule is the proportion of all transactions that contain both A and B [6], see equation (1).…”

Section: Theoretical Foundations Of Association Rulesmentioning

confidence: 99%

Research on association rule mining of specialty industries based on apriori algorithm

Chen,

Wang,

Wang

et al. 2024

Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023)

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With the continuous improvement of people's living standards and the intensification of electricity consumption in the whole society, rural specialty industries are facing various opportunities and challenges. This paper takes the characteristic industries of each county and city in Jilin Province as the research object, and utilizes Python to collect data and analyze the association rule mining. Firstly, this paper collects the daily electricity consumption of featured industries in each county and city in the past two years, constructs multi-dimensional datasets, and applies data mining methods to conduct data mining analysis. Secondly, we use the Apriori algorithm to analyze the association rules of the characteristic industries of each county and city under the big data of electricity. Finally, we provide useful suggestions and insights on the development of featured industries for rural revitalization through the analysis of association rules under electric power big data in each county and city.

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A Multi-Level Association Rule Mining Algorithm Based on NSGA-II for Market Basket Analysis

Wang

2023

2023 4th Information Communication Technologies Conference (ICTC)

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A Comparative Analysis of Apriori and FP-Growth Algorithms for Market Basket Analysis Using Multi-level Association Rule Mining

Cited by 3 publications

References 6 publications

Comorbidity Patterns and Management in Inpatients with Endocrine Diseases by Age Groups in South Korea: Nationwide Data

Comorbidity Patterns and Management in Inpatients with Endocrine Diseases by Age Groups in South Korea: Nationwide Data

Research on association rule mining of specialty industries based on apriori algorithm

A Multi-Level Association Rule Mining Algorithm Based on NSGA-II for Market Basket Analysis

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