Simulation of Sports Economic Development Using Data Mining and Statistical Modelling

2016 ◽  
Author(s):  
Guest Editor Jonathan Arnold
Keyword(s):  
Data Mining ◽  
Economic Development ◽  
Statistical Modelling ◽  
Using Data

Cognitive Social Mining Analysis Using Data Mining Techniques

2019 ◽  
pp. 99-117 ◽  
Author(s):  
Dharmpal Singh
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Statistical Modelling ◽  
Data Interpretation ◽  
Computer Mediated ◽  
The Social ◽  
Using Data ◽  
Collaborative Activities

Social media are based on computer-mediated technologies that smooth the progress of the creation and distribution of information, thoughts, idea, career benefits and other forms of expression via implicit communities and networks. The social network analysis (SNA) has emerged with the increasing popularity of social networking services like Facebook, Twitter, etc. Therefore, information about group cohesion, contribution in activities, and associations among subjects can be obtained from the analysis of the blogs. The analysis of the blogs required well-known knowledge discovery tools to help the administrator to discover participant collaborative activities or patterns with inferences to improve the learning and sharing process. Therefore, the goal of this chapter is to provide the data mining tools for information retrieval, statistical modelling and machine learning to employ data pre-processing, data analysis, and data interpretation processes to support the use of social network analysis (SNA) to improve the collaborative activities for better performance.

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.

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. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Forecasting Automobile Retail Sales Using Data Mining The Case of Ranchi, Jharkhand, India

2018 ◽  
Vol 6 (9) ◽  
pp. 572-574
Author(s):  
Gyaneshwar Mahto ◽  
Umesh Prasad ◽  
Rajiv Kumar Dwivedi
Keyword(s):  
Data Mining ◽  
Retail Sales ◽  
Using Data

Diabetes Prediction using Data Mining

2019 ◽  
Vol 7 (3) ◽  
pp. 749-753
Author(s):  
Suhasini Vijaykumar ◽  
Manjiri Moghe
Keyword(s):  
Data Mining ◽  
Diabetes Prediction ◽  
Using Data
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