Comparative Study of Top 10 Algorithms for Association Rule Mining

Nigam, Bhawna; Nigam, Aditya; Dalal, Poojan

doi:10.26438/ijcse/v5i8.190195

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…Comprehending the linguistic subtleties of Arabic, current methodologies for detecting ambiguity, and the use of ML in related fields is essential for the development of efficient ML algorithms to detect ambiguities in Arabic requirement documents. (Nigam et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms

Althunibat,

Alsawareah,

Maidin

et al. 2024

International Journal of Cloud Applications and Computing

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The identification of ambiguities in Arabic requirement documents plays a crucial role in requirements engineering. This is because the quality of requirements directly impacts the overall success of software development projects. Traditionally, engineers have used manual methods to evaluate requirement quality, leading to a time-consuming and subjective process that is prone to errors. This study explores the use of machine learning algorithms to automate the assessment of requirements expressed in natural language. The study aims to compare various machine learning algorithms according to their abilities in classifying requirements written in Arabic as decision tree. The findings reveal that random forest outperformed all stemmers, achieving an accuracy of 0.95 without employing a stemmer, 0.99 with the ISRI stemmer, and 0.97 with the Arabic light stemmer. These results highlight the robustness and practicality of the random forest algorithm.

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Section: Related Workmentioning

confidence: 99%

Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms

Althunibat,

Alsawareah,

Maidin

et al. 2024

International Journal of Cloud Applications and Computing

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“…1) Items and item sets: Let the item set 𝐼 = {𝑖 1 , 𝑖 2 , ⋯ , 𝑖 𝑘 } be the set of all items, where 𝑖 𝑘 (𝑘 = 1,2, ⋯ , 𝑚) is called a term, and the item set containing m items is called the m-item set [24].…”

Section: Definitions Related To Association Rule Mining Algorithmsmentioning

confidence: 99%

Exploring the path of integration of rural tourism culture and food industry in the context of big data

Liu

2023

Applied Mathematics and Nonlinear Sciences

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Under the background of big data, better integrating rural tourism culture and the food culture industry becomes an important research direction to improve the quality of rural tourism and promote rural revitalization. Based on the association rule mining algorithm under big data technology, this paper analyzes the keyword data of tourists for rural tourism culture and food culture on the Internet review platform by combining the survey and research on rural tourism culture resources and food culture resources in Y town. Results: The number of tourists participating in rural tourism was 51.2% for males and 48.8% for females. In terms of age composition, visitors aged 26-60 accounted for 82.5% of the total, thus indicating that middle-aged and older people in pursuit of nostalgia and local flavor dominate rural tourism. Regarding income level, 81.1% of the rural tourism tourists were in the middle and high end. In terms of tourists’ education, 36.1% of the tourists’ education level is undergraduate, while the rest of the education levels are 7.5%/11.2%/19%/26.2%, respectively, indicating that people with high education are more willing and know how to pursue the culture embedded in the countryside. From the big data sentiment analysis of tourists’ evaluation words, the most frequently appearing words are “special” and “delicious”, reaching 492 and 465 times, respectively, while the least frequent keyword is “dangerous”. The least frequent keyword is “dangerous”, but it also appears 61 times. From the viewpoint of experience, the overall experience of rural tourism culture and food culture of tourists is 76.41% and 79.81%. With the above analysis, in the process of integrating the development of rural tourism culture and food culture, we should focus on creating local characteristic brands, developing characteristic food culture, building characteristic rural tribes, and preventing uniformity from enhancing the attractiveness of the countryside and providing a new road for promoting rural revitalization.

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“…It is then possible to apply association rules methods. To compute the association rules we use apriori [37] and fpgrowth [38], influenced by comparative studies of association rules algorithms [39,40], presenting these as options for the reduction through association rules process.…”

Section: Reduction Through Association Rulesmentioning

confidence: 99%

Conclusive Local Interpretation Rules for Random Forests

Mollas¹,

Bassiliades²,

Tsoumakas³

2021

Preprint

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In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be able to provide clear interpretations for their decisions. Otherwise, their obscure decision-making processes can lead to socioethical issues as they interfere with people's lives. In the aforementioned sectors, random forest algorithms strive, thus their ability to explain themselves is an obvious requirement. In this paper, we present LionForests, which relies on a preliminary work of ours. LionForests is a random forest-specific interpretation technique, which provides rules as explanations. It is applicable from binary classification tasks to multi-class classification and regression tasks, and it is supported by a stable theoretical background. Experimentation, including sensitivity analysis and comparison with state-of-the-art techniques, is also performed to demonstrate the efficacy of our contribution. Finally, we highlight a unique property of LionForests, called conclusiveness, that provides interpretation validity and distinguishes it from previous techniques.

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Comparative Study of Top 10 Algorithms for Association Rule Mining

Cited by 5 publications

References 9 publications

Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms

Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms

Exploring the path of integration of rural tourism culture and food industry in the context of big data

Conclusive Local Interpretation Rules for Random Forests

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