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Bank Loan Analysis using Data Mining Techniques

Trinh Thi-Nhi, Nguyen Hoang-Diep

Annals of Computer Science and Information Systems

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2022

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Vol. 33

295--298

EN

Nowadays, a bank loan can provide people with cash to fund home improvements or start a business. However, some customers who are accepted with a loan cannot repay or someone usually repays in a delayed time. Therefore, to minimize losses, examining loan applications is particularly evident for the bank. This paper study on bank loan analysis using data mining techniques. We use association rules mining, clustering, and classification techniques on the applicant's profile to help the bank quickly decide for a loan applicant.

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Classification models based on association rules for estimation of key process variables in nuclear power plant

Narasimhan S., Rajendran Velayudham

Journal of Power Technologies

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2020

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Vol. 100, nr 4

315--330

EN

Nuclear power plant process systems have developed great lyover the years. As a large amount of data is generated from Distributed Control Systems (DCS) with fast computational speed and large storage facilities, smart systems have taken over analysis of the process. These systems are built using data mining concepts to understand the various stable operating regimes of the processes, identify key performance factors, makes estimates and suggest operators to optimize the process. Association rule mining is a frequently used data-mining conceptin e-commerce for suggesting closely related and frequently bought products to customers. It also has a very wide application in industries such as bioinformatics, nuclear sciences, trading and marketing. This paper deals with application of these techniques for identification and estimation of key performance variables of a lubrication system designed for a 2.7 MW centrifugal pump used for reactor cooling in a typical 500MWe nuclear power plant. This paper dwells in detail on predictive model building using three models based on association rules for steady state estimation of key performance indicators (KPIs) of the process. The paper also dwells on evaluation of prediction models with various metrics and selection of best model.

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Learning from student browsing data on e-learning platforms: case study

Cansiz Sergen, Sudan Berk, Ogretici Emre, Siddik Aktas Mehmet

Annals of Computer Science and Information Systems

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2020

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Vol. 22

37--44

EN

Interpretation of the behaviors of students in e-learning platforms with machine learning models has become an emerging need in recent years. Increase in the number of registered students on e-learning platforms is one of the reasons for choosing machine learning models. Tracking, modeling and understanding student activities gets more complex when the number of students is increased. This study is focusing modeling student activities on e-learning platforms with Complex Event Processing (CEP), Association Rule Mining (ARM) and Clustering methods based on distributed software architecture. Within the scope of this study, different modules that work real-time have been developed. An admin panel has been also developed in order to control all modules and track the student actions. Performance results of modules also obtained and evaluated on distributed system architecture.

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Evaluation of Apriori Algorithm on Retail Market Transactional Database to get Frequent Itemsets

Gaikwad Pooja R., Shailesh D. Kamble Shailesh D., Thakur Nileshsingh V., Patharkar Akshay S.

Annals of Computer Science and Information Systems

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2017

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Vol. 10

187--192

EN

In Data mining the concept of association rule mining (ARM) is used to identify the frequent itemsets from large datasets. It defines frequent pattern mining from larger datasets using Apriori algorithm \& FP-growth algorithm. The Apriori algorithm is a classic traditional algorithm for the mining all frequent itemsets and association rules. But, the traditional Apriori algorithm have some limitations i.e. there are more candidate sets generation \& huge memory consumption, etc. Still, there is a scope for improvement to modify the existing Apriori algorithm for identifying frequent itemsets with a focus on reducing the computational time and memory space. This paper presents the analysis of existing Apriori algorithms and results of the traditional Apriori algorithm. Experimentation carried out on transactional database i.e. retail market for getting frequent itemsets. The traditional Apriori algorithm is evaluated in terms of support and confidence of transactional itemsets.

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Analysis of Datamining Technique for Traffic Accident Severity Problem : A Review

Gupta Meenu, Solanki Vijender Kumar, Singh Vijay Kumar

Annals of Computer Science and Information Systems

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2017

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Vol. 10

197--199

EN

This paper is discussing about the road accident severity survey using data mining, where different approaches have been considered. We have collected research work carried out by different researchers based on road accidents. Article describing the review work in context of road accident case's using data mining approach. The article is consisting of collections of methods in different scenario with the aim to resolve the road accident. Every method is somewhere seeming to productive in some ways to decrease the no of causality. It will give a better edge to different country where the no of accidents is leading to fatality of life.