Plausible characteristics of association rule mining algorithms for e-commerce

Soni, Hemant Kumar; Sharma, Sanjiv; Jain, Manisha

doi:10.1109/aeeicb.2017.7972379

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…ARM is another prominent field of data mining that can be used to identify associations of data features. For example, Soni et al (2017) applied ARM to analyze e-commerce algorithms and identify the algorithm weaknesses. Based on the experimental results, the authors proposed potential characteristics for designing an efficient e-commerce database algorithm to support incremental and interactive ARM.…”

Section: Data Mining Applicationsmentioning

confidence: 99%

ARM for Analyzing Factors Influencing Vaccinations During the COVID-19 Outbreak

Buathong

Jarupunphol

2023

JDSIS

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This article investigates factors influencing Coronavirus 2019 (COVID-19) vaccinations and public concerns using association rule mining (ARM). The experiment was conducted in Phuket at the beginning of 2022 when many people were still not vaccinated. The online questionnaire comprises 26 questions with a sample of 403 participants living in Phuket, Thailand. Among the participants, 377 people were vaccinated. The experiment is based on two main ARM methods: Apriori and FP-Growth. The performance assessment criteria were 0.5 and 0.6 minimum support and 0.9 and 1.0 minimum confidence. The most appropriate values are based on 0.5 minimum support and 1.0 minimum confidence with lift > 1. The experimental results reveal factors influencing COVID-19 vaccinations in association rules. For example, most vaccinated people received Sinovac. They are female and knowledgeable about vaccines. They are concerned about severe illness or death and live in the Mueang district more than others. They are also concerned about injection site pain, nausea, vomiting, numbness, and hemiplegia. Enhancing herd immunity is another compelling reason for people to be vaccinated. In the meantime, these derived rules can be reversed to explain why many people are not vaccinated. For instance, the most available vaccine is Sinovac, and several vaccination points are in the Mueang district.

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Section: Data Mining Applicationsmentioning

confidence: 99%

ARM for Analyzing Factors Influencing Vaccinations During the COVID-19 Outbreak

Buathong

Jarupunphol

2023

JDSIS

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“…it uses graph with corresponding weight for rules for reducing the useless association rules. This method always based on the user interest and their own set of attributes with maximum efficiency [7].…”

Section: Issues In Association Rule Miningmentioning

confidence: 99%

“…Individual member of the group is consistent across each other with effective behavior. If there are any changes in the group response will provides the accurate data to the peers in the corresponding group [7]. Social networking changes the entire people life such as representation of data, groups and communication between the person etc.…”

Section: Issues In Frequent Pattern In Social Networkingmentioning

confidence: 99%

Social Networking Data Research Using Frequent Pattern Mining and Machine Learning Data

Lakshmi¹,

Krishnamurthy²

2019

IJEAT

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The data are generated by the sources are very large in number with variety of form. These data are organized in to specific format in order to handle properly. Data mining methods are addressed various problem during data extraction process to analytical process. The relevant data are extracted by applying pattern over the huge databases. Association rule mining introduces the method to extracts the related data from the datasets using the performance metrics like support and confidence. Traditional algorithm uses this metrics which is restricted to common attribute format. This problem is addressed by using generic attribute format with frequent pattern mining. The main objective of the paper is to analyze the algorithm and performance metrics related to the frequent patter mining or relevant data. Association rule mining has analyzed with various parameters in single connectivity and multi connectivity rules. Social networking suffers various problem because of uncertain data arrived for processing which is analyzed with various efficiency related elements. The analysis and prediction are also compared with the machine algorithms like classification and clustering and so on. Various frequent pattern mining algorithm is analyzed and review has been carried out based on the performance level.

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“…Apriori-based algorithms work in two phases, first to identify frequent patterns and second generate rules from these frequent patterns [7]. Association rule mining helpful in the various domain including e-commerce and time-series data analysis [19][20].…”

Section: Related Workmentioning

confidence: 99%

An Empirical Algorithm for High and Low Correlative Association Rule Mining

Soni¹,

Sharma²,

Upadhyay³

2018

IJIES

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Support and confidence based Association rules mining algorithms have certain problems. Although other metrics like interest factor, comprehensibility, lift, correlation etc. are available to measure the interestingness of association rules. All the objectives are not suitable for each and every situation. All the objectives which were proposed in the literature, have some drawback, like correlation analysis gives equal importance to the items those are present and absent in transaction database. Resultant the rules generated by this, sometime mislead decision makers. Hence there is a strong need to define some new objectives for association rules that support in effective decision making. In this paper, authors proposed two novel objectives, high correlation and low correlation for 2-variables and 3-variables. These novel objectives clearly indicate that how much or how less two/three items are correlated. On the basis of this, decision makers can form their business strategies. An empirical algorithm for high and low correlative association rules generation is also proposed. With numeric evolution and experiments on the real-life data set, effectiveness has been measured and found that proposed algorithm gives better results.

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Plausible characteristics of association rule mining algorithms for e-commerce

Cited by 11 publications

References 7 publications

ARM for Analyzing Factors Influencing Vaccinations During the COVID-19 Outbreak

ARM for Analyzing Factors Influencing Vaccinations During the COVID-19 Outbreak

Social Networking Data Research Using Frequent Pattern Mining and Machine Learning Data

An Empirical Algorithm for High and Low Correlative Association Rule Mining

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