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DATA MINING TECHNIQUES
| Institution | UNIVERSITY |
| Course | FORENSICS |
| Year | 1st Year |
| Semester | Unknown |
| Posted By | Brian Mike |
| File Type | |
| Pages | 21 Pages |
| File Size | 545.18 KB |
| Views | 4972 |
| Downloads | 0 |
| Price: |
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Description
The paper presents application of data mining techniques to fraud analysis. We
present some classification and prediction data mining techniques which we
consider important to handle fraud detection. There exist a number of data
mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models
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THE ACARI (ticks & mites)
One of the largest, highly diverse and
widely distributed groups in the animal
kingdom
• Very abundant - numbers extremely high
(more than 60,000 described species
(with an estimated 500,000 more still
undescribed)
• Habitat - both aquatic and terrestrial
(many free-living & parasitic
82 Pages
376 Views
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4.2 MB