A Dynamic and Scalable Decision Tree Based Mining of Educational Data

2020 ◽  
pp. 841-866
Author(s):  
Dineshkumar B. Vaghela ◽  
Priyanka Sharma ◽  
Kalpdrum Passi
Keyword(s):  
Data Mining ◽  
Weather Forecasting ◽  
Biology Education ◽  
Big Data Analysis ◽  
Environmental Research ◽  
Distributed Environment ◽  
Web Based ◽  
Data Mining Approach ◽  
Use Of Internet ◽  
Scalable Data Mining

The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.

A Dynamic and Scalable Decision Tree Based Mining of Educational Data

2017 ◽  
pp. 248-273
Author(s):  
Dineshkumar B. Vaghela ◽  
Priyanka Sharma ◽  
Kalpdrum Passi
Keyword(s):  
Data Mining ◽  
Weather Forecasting ◽  
Biology Education ◽  
Big Data Analysis ◽  
Environmental Research ◽  
Distributed Environment ◽  
Web Based ◽  
Data Mining Approach ◽  
Use Of Internet ◽  
Scalable Data Mining

The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

scholarly journals Explaining Individuals’ Usage of Social Commerce: A Data Mining Approach

2018 ◽  
Vol 12 (8) ◽  
pp. 116
Author(s):  
Yazan Alshamaila ◽  
Ibrahim Aljarah ◽  
Ala’ M. Al-Zoubi
Keyword(s):  
Data Mining ◽  
Economic Value ◽  
Radical Change ◽  
Consumer Involvement ◽  
Social Commerce ◽  
Initial Attempt ◽  
Web Based ◽  
Data Mining Approach ◽  
Shopping Experience ◽  
Civil Status

With the use of Web 2.0 technology, e-commerce is undergoing a radical change that enriches consumer involvement and enables a better understanding of economic value. This emerging phenomenon is known as social commerce. Social commerce (s-commerce) presents a new alternative for consumers to search for and find information about products they are seeking to buy. In spite of its universality, the adoption of this burgeoning technology is affected by several factors. This research project is an initial attempt to explore individuals’ intention of s-commerce usage through the data mining approach. The data was collected via a web-based questionnaire survey of 360 social network site (SNS) users in Jordan. Data mining techniques were then used to analyze the collected data in order to figure out what group of features is best for predicting s-commerce adoption among SNS users. The results showed that data characteristics related to gender, monthly income, civil status, number of connections, and prior online shopping experience are key factors in the classification process. The findings may assist researchers in investigating social commerce issues and aid practitioners in developing new s-commerce strategies. 

A data-mining approach for product conceptualization in a web-based architecture

2009 ◽  
Vol 60 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Wei Yan ◽  
Chun-Hsien Chen ◽  
Youfang Huang ◽  
Weijian Mi
Keyword(s):  
Data Mining ◽  
Web Based ◽  
Data Mining Approach
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