Discussion of 'Data mining journal entries for fraud detection: An exploratory study'

2010 ◽  
Vol 11 (3) ◽  
pp. 186-188 ◽  
Author(s):  
Eckhardt Kriel CA (SA)
2020 ◽  
Vol 214 ◽  
pp. 01014
Author(s):  
Zhao Yingju

For the fixed tourist routes in the scenic spot, the longer the journey is, the slower the speed is, and the easier the congestion is. This study is an exploratory study. In this paper, Yudaokou grassland forest scenic area, a nature reserve crossed by national No.1 scenic road, is selected as the research object. Based on the point- axis gradual diffusion theory, the mobile app is used to record the travel process of tourists on the same tourist route in the same scenic area, so as to calculate the travel speed of tourists on different road sections, and then predict the future congestion of the scenic area, the smaller the speed, the greater the probability of congestion; the greater the speed, the less time, the smaller the probability of congestion. Finally, the paper discusses the significance and theoretical contribution of the study on the two aspects of tourists’ moving behavior and moving mode in scenic spots.


2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.


Web Services ◽  
2019 ◽  
pp. 618-638
Author(s):  
Goran Klepac ◽  
Kristi L. Berg

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.


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