The Survey of Data Mining Applications and Feature Scope

Padhy, Neelamadhab; Mishra, Pragnyaban; Panigrahi, Rasmita

doi:10.5121/ijcseit.2012.2303

Cited by 103 publications

(61 citation statements)

References 22 publications

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“…The problem of outlier detection has also been addressed by the database and data mining communities [16], aiming at solving the problem of scalability. The Banking Industries have not been spared from the need to keep up with their constantly changing industry to stay viable and competitive [17]. Systems previously involved manual recording of branch transactions and the generation of reports from manual ledgers according to [18] these had to be consolidated with other branches into a final report.…”

Section: Related Workmentioning

confidence: 99%

Fraud Detection on Bulk Tax Data Using Business Intelligence Data Mining Tool: A Case of Zambia Revenue Authority

Mwanza¹

2016

International Journal of Advanced Research in Computer and Comm

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Zambia Revenue Authority (ZRA) generates large volumes of data that need complex mechanisms in order to extract useful tax information. The purpose of the study was to develop a data mining model for detection of fraud on tax and taxpayer data for ZRA. This study focused on two areas. These were (1) the baseline study that helped to establish the extent of the challenges in fraud detection for the tax payers and (2) the automation and development of the fraud detection tool using the results from the baseline study. Our baseline study showed that the current methodologies, processes, architectures, and technologies that were being used to transform raw data into meaningful and useful information were tedious and time consuming. In order to detect fraud they depended on random audits, informants and under-cover operations. A model which implements outlier algorithms for fraud detection, Continuous Monitoring of Distance Based and Distance Based Outlier Queries was then developed. We used both algorithms to analyse the domestic tax payments to detect underpayments and overpayments according to business rules. Underpayments and overpayments are marked as outliers. Results generated by our tool showed improved accuracy and takes less time in order to detect under and over payments as outliers when compared to the older methods.

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Section: Related Workmentioning

confidence: 99%

Fraud Detection on Bulk Tax Data Using Business Intelligence Data Mining Tool: A Case of Zambia Revenue Authority

Mwanza¹

2016

International Journal of Advanced Research in Computer and Comm

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“…On the other hand, data transformation could be a part of data pre-processing step, In addition, data compression techniques such as Principal Component Analysis (PCA) to obtain manageable sizes of data have been also commonly utilized for data reduction [4,22,23]. PCA, used for dimension reduction, is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.…”

Section: Quality Data Pre-processingmentioning

confidence: 99%

“…Although there is less attention regarding these steps in KDD process in the literature. Below, some explanations are provided regarding data preparation and preprocessing steps [22].…”

Section: Dm Application In Qamentioning

confidence: 99%

“…This section provides a brief description of some studies regarding the application of DM in the quality tasks mentioned in the beginning of this section. Interested readers are referred to [4,[22][23] for comprehensive review from 2007 to date.…”

Section: Dm In Quality Datamentioning

confidence: 99%

“…This method is also common in the literature and these data are obtained during online sensor measurements or from historical logs. Alternatively, process simulations and simulated data sets also existed and these were used to test the performance of the algorithms used [4,22,23].…”

Section: Quality Data Preparationmentioning

confidence: 99%

See 2 more Smart Citations

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Rostami

Dantan

Homri

2015

Int. J. Metrol. Qual. Eng.

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. AbstractIn many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases.Due to advances in data collection systems and analysis tools, data mining (DM)has widely been applied for quality assessment (QA) in manufacturing industries.In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set.Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

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Portability analysis of data mining models for fog events forecasting

Zazzaro

2021

Statistical Analysis

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This article describes an analytical method for comparing geographical sites and transferring fog forecasting models, trained by Data Mining techniques on a fixed site, across Italian airports. This portability method uses a specific intersite similarity measure based on the Euclidean distance between the performance vectors associated with each airport site. Performance vectors are useful for characterizing geographical sites. The components of a performance vector are the performance metrics of an Ensemble descriptive model. In the tests carried out, the comparison method provided very promising results, and the forecast model, when applied and evaluated on a new compatible site, shows only a small decrease in performance. The portability schema provides a meta‐learning methodology for applying predictive models to new sites where a new model cannot be trained from scratch owing to the class imbalance problem or the lack of data for a specific learning. The methodology offers a measure for clustering geographical sites and extending weather knowledge from one site to another.

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The Survey of Data Mining Applications and Feature Scope

Cited by 103 publications

References 22 publications

Fraud Detection on Bulk Tax Data Using Business Intelligence Data Mining Tool: A Case of Zambia Revenue Authority

Fraud Detection on Bulk Tax Data Using Business Intelligence Data Mining Tool: A Case of Zambia Revenue Authority

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Portability analysis of data mining models for fog events forecasting

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