scholarly journals Employing Data Mining Techniques in Testing the Effectiveness of Modernization Theory

2016 ◽  
Vol 2 (1) ◽  
pp. 98
Author(s):  
Tolga Aydın
Keyword(s):  
Data Mining ◽  
Economic Development ◽  
Mobile Phone ◽  
Important Variable ◽  
The Internet ◽  
Modernization Theory ◽  
Data Mining Techniques ◽  
Mobile Phone Usage ◽  
Using Data ◽  
New Variables

This interdisciplinary study is concerned with testing the effectiveness of Modernization Theory in explaining regime change by means of data mining techniques. Modernization Theory, which links democratization with economic development (improvements in income, urbanization, industrialization, education and communication levels), has been criticized widely. Many criticisms posited that there is not a significant relation between economic development and democratization. This study is an attempt to test whether the theory has improved its effectiveness with the advent of the Internet and mobile phone technologies. To this end, first, the variables are introduced. Then, the study makes an analysis by using data mining techniques. It first tests the correlation between democratization and improvements in income, education, urbanization and communication levels within the period between 1976 and 1995. Then it adds the new variables, the Internet and mobile phone usage, and tests the correlation between democratization and this new range of variables for 1996-2015 period. In the conclusion, the study evaluates whether the effectiveness of Modernization Theory is improved when the Internet and mobile phone usage are added as the new variables. It is found that there is not a strong relation between income per capita and democratization as some critics of the Modernization Theory suggest, but other factors emphasized by this theory like improvements in education and communication have a more decisive effect. Moreover, among our new variables, Internet usage proved to be a really important variable conducive to democratization according to test results.

scholarly journals Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia

2016 ◽  
Vol 20 ◽  
Author(s):  
Md Zahidul Islam ◽  
Steven D’Alessandro ◽  
Michael Furner ◽  
Lester Johnson ◽  
David Gray ◽  
...  
Keyword(s):  
Data Mining ◽  
Mobile Phone ◽  
Domain Knowledge ◽  
Service Providers ◽  
Mobile Service ◽  
Data Mining Techniques ◽  
Brand Switching ◽  
Switching Patterns ◽  
Switching Models ◽  
Mobile Phone Usage

There is more than one mobile-phone subscription per member of the Australian population. The number of complaints against the mobile-phone-service providers is also high. Therefore, the mobile service providers are facing a huge challenge in retaining their customers. There are a number of existing models to analyse customer behaviour and switching patterns. A number of switching models may also exist within a large market. These models are often not useful due to the heterogeneous nature of the market. Therefore, in this study we use data mining techniques to let the data talk to help us discover switching patterns without requiring us to use any models and domain knowledge. We use a variety of decision tree and decision forest techniques on a real mobile-phone-usage dataset in order to demonstrate the effectiveness of data mining techniques in knowledge discovery. We report many interesting patterns, and discuss them from a brand-switching and marketing perspective, through which they are found to be very sensible and interesting.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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