Data mining technique has a key role in knowledge
extraction from databases to promote efficient decision making.
This paper presents an approach for knowledge extraction from
a sample database of some school dropped students using
association rule generation and classification algorithms to
demonstrate how knowledge-based development policy making
decisions can be processed from the extracted knowledge. A
system architecture is proposed considering mobile computing
devices as user interface to the system connecting mass people
database with cloud computing environment resources. The
causes of education termination are investigated by analyzing the
sample database in terms of attribute value relationship in the
form of association rules to reason about the causes based on the
computed support and confidence. It is observed that if the
affected family had no service holders, the dropped student had
to stop his education because of financial problem. Classification
is applied to classify the dropped students in different groups
based on their level of education.
A Review of Big Data Analytics in Sector of Higher EducationIJERA Editor
This paper is about the use of big data analytics in higher education. In this paper, we see what the big data is and where does it come from. We will also try to find why the big data analytics has become a buzzword in almost every sector today through our literature review on the big data analytics and its applications in higher education sector. Then we see what the big educational data is, how it is generated and analyzed. We found that the two most important types of analytics are- Learning and academic analytics which will be discussed. Several papers describe the benefits of implementation of analytics in the education sector and the opportunities provided which will be discussed in this paper. We also found that the basic characteristics such as size, speed, variety and some other factors are responsible for some issues and challenges to the use of analytics in this sector. We will discuss those issues and challenges and discuss some proposed solutions to address them.
Big Data Analytics using in the Field of Education Systemijtsrd
This paper is a study on the use of big data in education analyzed how the big data and open data technology can actually involve in educational system. Present days we analyze how big mounts of unused data can benefit and improve to education sector. Big data has dramatically changed the ways in which leaders make decisions in natural science, Agriculture science, banking and retail business, healthcare and in education. In educations sector wide verity of digital data produced in every institution. For example the forms of data like videos, texts, voices etc. the digital educations improves both teachers and students understandings and improve teaching effectiveness. In education big data we use econometrics, causal inference models, social network analysis, text analysis, and linguistic analysis methods. Using different types of technologies adopting in education are mobile devices, teleconferences and remote access systems, educational platforms and services. This method is effectively used by students, teachers, academic faculty, specialists, and researchers in education. Gagana H. S | Sandhya B N | Gouthami H. S "Big Data Analytics using in the Field of Education System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31196.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31196/big-data-analytics-using-in-the-field-of-education-system/gagana-h-s
Application of Savings and Loan Cooperative ServicesYogeshIJTSRD
Information technology knowledge has become a necessity. Most of a persons daily activities involve the assistance of information technology, both teaching and learning activities, working in institutions and entrepreneurship. In addition, knowledge of information technology is a persons main asset to be able to compete in the digital era. A cooperative is an institution that runs on the principle of kinship. So that in carrying out its activities prioritizing the welfare of its members and aiming to increase the economic growth of the community. The goal to be achieved in this community service activity is to produce an android based system of savings and loan cooperative service that is able to facilitate the delivery of information from management to members in real time and can provide solutions for speed, accuracy and accuracy. The method used in this research and development research uses the waterfall model, which is a process or steps to develop a new product or improve an existing product. The system analysis was carried out with the initial stages of the research method, the communication stage was carried out by the interview and observation process. The observation process is carried out by making direct observations to get an overview and the interview process is carried out by conducting questions and answers to match the data and information. After designing the next step is build an android system based of savings and loan cooperative service. Dimas Indra Laksmana | Maranatha Wijayaningtyas | Kiswandono "Application of Savings and Loan Cooperative Services" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd40065.pdf Paper URL: https://www.ijtsrd.com/management/mis-and-retail-management/40065/application-of-savings-and-loan-cooperative-services/dimas-indra-laksmana
School Management Information Systems: Challenges to Educational Decision-Mak...IJITE
Despite the benefits of school management information systems (SMIS), the concept of data-driven school culture failed to materialize for many educational institutions. Challenges posed by the quality of data in the big data era have prevented many schools from realizing the real potential of the SMIS. The paper analyses the uses, features, and inhibiting factors of SMIS. The paper proposes a five-phase conceptual model that assist administrators with making timely, quality decisions. The paper enriches the theoretical landscape of SMIS usage in the era of big data and lays a foundation for the future by establishing an educational decision-making model.
Higher education institutions now a days are operating in an increasingly complex and
competitive environment. The application of innovation is a must for sustaining its competitive advantage.
Institution leaders are using data management and analytics to question the status quo and develop effective
solutions. Achieving these insights and information requires not a single report from a single system, but
rather the ability to access, share, and explore institution-wide data that can be transformed into meaningful
insights at every level of the institution. Consequently, institutions are facing problems in providing necessary
information technology support for fulfilling excellence in performance. More specifically, the best practices
of big data management and analytics need to be considered within higher education institutions. Therefore,
the study aimed at investigating big data and analytics, in terms of: (1) definition; (2) its most important
principles; (3) models; and (4) benefits of its use to fulfill performance excellence in higher education
institutions. This involves shedding light on big data and analytics models and the possibility of its use in
higher education institutions, and exploring the effect of using big data and analytics in achieving performance
excellence. To reach these objectives, the researcher employed a qualitative research methodology for
collecting and analyzing data. The study concluded the most important result, that there is a significant
relationship between big data and analytics and excellence of performance as big data management and
analytics mainly aims at achieving tasks quickly with the least effort and cost. These positive results support
the use of big data and analytics in institutions and improving knowledge in this field and providing a practical
guide adaptable to the institution structure. This paper also identifies the role of big data and analytics in
institutions of higher education worldwide and outlines the implementation challenges and opportunities in the
education industry.
Influence User Involvement On The Quality Of Accounting Information SystemArnol Awal
Background of this study was based on the argument that there were correlation between user involvement and quality of accounting information system. This study aims to examine: the influence of user involvement on the quality of the information system
Companies desires for making productive discoveries from big data have motivated academic institutions offering variety of different data science (DS) programs, in order to increases their graduates' ability to be data scientists who are capable to face the challenges of the new age. These data science programs represent a combination of subject areas from several disciplines. There are few studies have examined data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that investigate DS programs and explore its curriculum structure across disciplines. Therefore, this study examines data science programs offered by American universities. The study aims to depict the current state of data science education in the U.S. to explore what discipline DS programs covers at the graduate level. The current study conducted an exploratory content analysis of 30 DS programs in the United States from a variety of disciplines. The analysis was conducted on course titles and course descriptions level. The study results indicate that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. In addition, all DS programs requires the basic knowledge of database design, representation, extraction and management. Furthermore, DS programs delivered information skills through their core courses. Moreover, the study results show that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Additionally, DS programs required courses also addressed communication visualization and mathematics skills.
A Review of Big Data Analytics in Sector of Higher EducationIJERA Editor
This paper is about the use of big data analytics in higher education. In this paper, we see what the big data is and where does it come from. We will also try to find why the big data analytics has become a buzzword in almost every sector today through our literature review on the big data analytics and its applications in higher education sector. Then we see what the big educational data is, how it is generated and analyzed. We found that the two most important types of analytics are- Learning and academic analytics which will be discussed. Several papers describe the benefits of implementation of analytics in the education sector and the opportunities provided which will be discussed in this paper. We also found that the basic characteristics such as size, speed, variety and some other factors are responsible for some issues and challenges to the use of analytics in this sector. We will discuss those issues and challenges and discuss some proposed solutions to address them.
Big Data Analytics using in the Field of Education Systemijtsrd
This paper is a study on the use of big data in education analyzed how the big data and open data technology can actually involve in educational system. Present days we analyze how big mounts of unused data can benefit and improve to education sector. Big data has dramatically changed the ways in which leaders make decisions in natural science, Agriculture science, banking and retail business, healthcare and in education. In educations sector wide verity of digital data produced in every institution. For example the forms of data like videos, texts, voices etc. the digital educations improves both teachers and students understandings and improve teaching effectiveness. In education big data we use econometrics, causal inference models, social network analysis, text analysis, and linguistic analysis methods. Using different types of technologies adopting in education are mobile devices, teleconferences and remote access systems, educational platforms and services. This method is effectively used by students, teachers, academic faculty, specialists, and researchers in education. Gagana H. S | Sandhya B N | Gouthami H. S "Big Data Analytics using in the Field of Education System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31196.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31196/big-data-analytics-using-in-the-field-of-education-system/gagana-h-s
Application of Savings and Loan Cooperative ServicesYogeshIJTSRD
Information technology knowledge has become a necessity. Most of a persons daily activities involve the assistance of information technology, both teaching and learning activities, working in institutions and entrepreneurship. In addition, knowledge of information technology is a persons main asset to be able to compete in the digital era. A cooperative is an institution that runs on the principle of kinship. So that in carrying out its activities prioritizing the welfare of its members and aiming to increase the economic growth of the community. The goal to be achieved in this community service activity is to produce an android based system of savings and loan cooperative service that is able to facilitate the delivery of information from management to members in real time and can provide solutions for speed, accuracy and accuracy. The method used in this research and development research uses the waterfall model, which is a process or steps to develop a new product or improve an existing product. The system analysis was carried out with the initial stages of the research method, the communication stage was carried out by the interview and observation process. The observation process is carried out by making direct observations to get an overview and the interview process is carried out by conducting questions and answers to match the data and information. After designing the next step is build an android system based of savings and loan cooperative service. Dimas Indra Laksmana | Maranatha Wijayaningtyas | Kiswandono "Application of Savings and Loan Cooperative Services" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd40065.pdf Paper URL: https://www.ijtsrd.com/management/mis-and-retail-management/40065/application-of-savings-and-loan-cooperative-services/dimas-indra-laksmana
School Management Information Systems: Challenges to Educational Decision-Mak...IJITE
Despite the benefits of school management information systems (SMIS), the concept of data-driven school culture failed to materialize for many educational institutions. Challenges posed by the quality of data in the big data era have prevented many schools from realizing the real potential of the SMIS. The paper analyses the uses, features, and inhibiting factors of SMIS. The paper proposes a five-phase conceptual model that assist administrators with making timely, quality decisions. The paper enriches the theoretical landscape of SMIS usage in the era of big data and lays a foundation for the future by establishing an educational decision-making model.
Higher education institutions now a days are operating in an increasingly complex and
competitive environment. The application of innovation is a must for sustaining its competitive advantage.
Institution leaders are using data management and analytics to question the status quo and develop effective
solutions. Achieving these insights and information requires not a single report from a single system, but
rather the ability to access, share, and explore institution-wide data that can be transformed into meaningful
insights at every level of the institution. Consequently, institutions are facing problems in providing necessary
information technology support for fulfilling excellence in performance. More specifically, the best practices
of big data management and analytics need to be considered within higher education institutions. Therefore,
the study aimed at investigating big data and analytics, in terms of: (1) definition; (2) its most important
principles; (3) models; and (4) benefits of its use to fulfill performance excellence in higher education
institutions. This involves shedding light on big data and analytics models and the possibility of its use in
higher education institutions, and exploring the effect of using big data and analytics in achieving performance
excellence. To reach these objectives, the researcher employed a qualitative research methodology for
collecting and analyzing data. The study concluded the most important result, that there is a significant
relationship between big data and analytics and excellence of performance as big data management and
analytics mainly aims at achieving tasks quickly with the least effort and cost. These positive results support
the use of big data and analytics in institutions and improving knowledge in this field and providing a practical
guide adaptable to the institution structure. This paper also identifies the role of big data and analytics in
institutions of higher education worldwide and outlines the implementation challenges and opportunities in the
education industry.
Influence User Involvement On The Quality Of Accounting Information SystemArnol Awal
Background of this study was based on the argument that there were correlation between user involvement and quality of accounting information system. This study aims to examine: the influence of user involvement on the quality of the information system
Companies desires for making productive discoveries from big data have motivated academic institutions offering variety of different data science (DS) programs, in order to increases their graduates' ability to be data scientists who are capable to face the challenges of the new age. These data science programs represent a combination of subject areas from several disciplines. There are few studies have examined data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that investigate DS programs and explore its curriculum structure across disciplines. Therefore, this study examines data science programs offered by American universities. The study aims to depict the current state of data science education in the U.S. to explore what discipline DS programs covers at the graduate level. The current study conducted an exploratory content analysis of 30 DS programs in the United States from a variety of disciplines. The analysis was conducted on course titles and course descriptions level. The study results indicate that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. In addition, all DS programs requires the basic knowledge of database design, representation, extraction and management. Furthermore, DS programs delivered information skills through their core courses. Moreover, the study results show that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Additionally, DS programs required courses also addressed communication visualization and mathematics skills.
Big data analytics and its impact on internet usersStruggler Ever
Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
Social navigation can be considered as an effective approach for supporting information management issues particularly privacy and security concern that prevail in the management of information systems. Any of these management information system security issues are a matter of critical acknowledgement for knowledge management systems as well. This paper outlines multiple privacy and security risks that can be applied to information systems in general. Including examples from different sectors such as health care, public information, and e-commerce, these management information issues provide an outline of the present situation. It also observes the key concerns of information system executive in these areas, highlighting the identification and explanation of regional differences and similarities. Selecting on a current issue in management information system the paper provides a detailed amount of knowledge on the possible reasons for the issues such as factors of economic development, technological status, and political/legal environment. The paper concludes by providing a revised framework for the management information issues formulated with effective literature searches. This is going to be effective enough for the studies that will more likely support such reasoning in the future.
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.Reraser Juan José Calderón
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.R. Department of Computer Science, Christ University, Bangalore, India Department of Computer Science , Jain University, Bangalore, India
Data science for digital culture improvement in higher education using K-mean...IJECEIAES
This study aims to investigate the meaningful pattern that can be used to improve digital culture in higher education based on parameters of the technology acceptance model (TAM). The methodology used is the data mining technique with K-means algorithm and text analytics. The experiment using questionnaire data with 2887 respondents in Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung. The data analysis and clustering result show that the perceived usefulness and behavioral intention to use information systems are above the normal value, while the perceived ease of use and actual system use is quite low. Strengthened with text analytics, this research found that the EDA and K-means result in harmony with the hope or desire of academic society the information system implementation. This research also found how important the socialization and guidance of information systems, especially the new one information system, in order to improve digital culture in higher education.
ow-a-days data volumes are growing rapidly in several domains. Many factors have contributed to this growth, including inter alia proliferation of observational devices, miniaturization of various sensors ,improved logging and tracking of systems, and improvements in the quality and capacity of both disk storage and networks .Analyzing such data provides insights that can be used to guide decision making. To be effective, analysis must be timely and cope with data scales. The scale of the data and the rates at which they arrive make manual inspection infeasible. As an educational management tool, predictive analytics can help and improve the quality of education by letting decision makers address critical issues such as enrollment management and curriculum Development. This paper presents an analytical study of this approach’s prospects for education planning. The goals of predictive analytics are to produce relevant information, actionable insight, better outcomes, and smarter decisions, and to predict future events by analyzing the volume, veracity, velocity, variety, value of large amounts of data and interactive exploration.
Big Data Analytics in Higher Education: A Reviewtheijes
Big Data provides an opportunity to educational Institutions to use their Information Technology resources strategically to improve educational quality and guide students to higher rates of completion, and to improve student persistence and outcomes. This paper explores the attributes of big data that are relevant to educational institutions, investigates the factors influencing adoption of big data and analytics in learning institutions and seeks to establish the limiting factors hindering use of big data in Institutions of higher learning. The study has been conducted through a desk search and reviewed sources of literature including scientific research journals and reports. The paper is based on desk research. The sources of literature that were reviewed included scientific research articles and journals, conference reports and theses. Online journals found on the internet were also examined with the search being broadened by Google Scholar where the following keywords were used “big data”, “developing countries”, “education systems” and “clustering”. The paper concludes that Big Data is important since it offers Universities opportunities to their Information Technology resources strategically to improve educational quality and guide students, colleges and universities see value in analytics; and therefore recommends that these institutions carry out investments in analytics programs and in people to have relevant data science. This is because Big Data can afford educational institutions opportunities to shape a modern and dynamic education system, in which every individual student can have the maximum benefit from, and can greatly contribute towards improving the quality of education
A Survey on Educational Data Mining TechniquesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction
.
INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...ijcsit
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction.
Big data analytics and its impact on internet usersStruggler Ever
Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
Social navigation can be considered as an effective approach for supporting information management issues particularly privacy and security concern that prevail in the management of information systems. Any of these management information system security issues are a matter of critical acknowledgement for knowledge management systems as well. This paper outlines multiple privacy and security risks that can be applied to information systems in general. Including examples from different sectors such as health care, public information, and e-commerce, these management information issues provide an outline of the present situation. It also observes the key concerns of information system executive in these areas, highlighting the identification and explanation of regional differences and similarities. Selecting on a current issue in management information system the paper provides a detailed amount of knowledge on the possible reasons for the issues such as factors of economic development, technological status, and political/legal environment. The paper concludes by providing a revised framework for the management information issues formulated with effective literature searches. This is going to be effective enough for the studies that will more likely support such reasoning in the future.
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.Reraser Juan José Calderón
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.R. Department of Computer Science, Christ University, Bangalore, India Department of Computer Science , Jain University, Bangalore, India
Data science for digital culture improvement in higher education using K-mean...IJECEIAES
This study aims to investigate the meaningful pattern that can be used to improve digital culture in higher education based on parameters of the technology acceptance model (TAM). The methodology used is the data mining technique with K-means algorithm and text analytics. The experiment using questionnaire data with 2887 respondents in Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung. The data analysis and clustering result show that the perceived usefulness and behavioral intention to use information systems are above the normal value, while the perceived ease of use and actual system use is quite low. Strengthened with text analytics, this research found that the EDA and K-means result in harmony with the hope or desire of academic society the information system implementation. This research also found how important the socialization and guidance of information systems, especially the new one information system, in order to improve digital culture in higher education.
ow-a-days data volumes are growing rapidly in several domains. Many factors have contributed to this growth, including inter alia proliferation of observational devices, miniaturization of various sensors ,improved logging and tracking of systems, and improvements in the quality and capacity of both disk storage and networks .Analyzing such data provides insights that can be used to guide decision making. To be effective, analysis must be timely and cope with data scales. The scale of the data and the rates at which they arrive make manual inspection infeasible. As an educational management tool, predictive analytics can help and improve the quality of education by letting decision makers address critical issues such as enrollment management and curriculum Development. This paper presents an analytical study of this approach’s prospects for education planning. The goals of predictive analytics are to produce relevant information, actionable insight, better outcomes, and smarter decisions, and to predict future events by analyzing the volume, veracity, velocity, variety, value of large amounts of data and interactive exploration.
Big Data Analytics in Higher Education: A Reviewtheijes
Big Data provides an opportunity to educational Institutions to use their Information Technology resources strategically to improve educational quality and guide students to higher rates of completion, and to improve student persistence and outcomes. This paper explores the attributes of big data that are relevant to educational institutions, investigates the factors influencing adoption of big data and analytics in learning institutions and seeks to establish the limiting factors hindering use of big data in Institutions of higher learning. The study has been conducted through a desk search and reviewed sources of literature including scientific research journals and reports. The paper is based on desk research. The sources of literature that were reviewed included scientific research articles and journals, conference reports and theses. Online journals found on the internet were also examined with the search being broadened by Google Scholar where the following keywords were used “big data”, “developing countries”, “education systems” and “clustering”. The paper concludes that Big Data is important since it offers Universities opportunities to their Information Technology resources strategically to improve educational quality and guide students, colleges and universities see value in analytics; and therefore recommends that these institutions carry out investments in analytics programs and in people to have relevant data science. This is because Big Data can afford educational institutions opportunities to shape a modern and dynamic education system, in which every individual student can have the maximum benefit from, and can greatly contribute towards improving the quality of education
A Survey on Educational Data Mining TechniquesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction
.
INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...ijcsit
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction.
A study model on the impact of various indicators in the performance of stude...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Running Head CONSUMER BEHAVIOR ANALYSISCONSUMER BEHAVIOR ANALMalikPinckney86
Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8
PROPOSAL
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database. The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
I. Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
II. Businesses also determine how the business environment affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
IV. Companies also find out how customer motivation affects customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing strategies based on the available data that they will gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors. However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary ...
Supervised and unsupervised data mining approaches in loan default prediction IJECEIAES
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Given the scenario, your role, and the information provided by theMatthewTennant613
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techniques on educational data of a higher education institution in Croatia. Data used for the analysis are event logs
downloaded from an e-learning environment of a real e-course. Data mining ...
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Knowledge Extraction by Applying Data Mining Technique to Use in Decision Making for Development Policy Making
1. Knowledge Extraction by Applying Data Mining
Technique to Use in Decision Making for
Development Policy Making
Md. Humayun Kabir
Dept. of Computer Science and Engineering
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
Abstract— Data mining technique has a key role in knowledge
extraction from databases to promote efficient decision making.
This paper presents an approach for knowledge extraction from
a sample database of some school dropped students using
association rule generation and classification algorithms to
demonstrate how knowledge-based development policy making
decisions can be processed from the extracted knowledge. A
system architecture is proposed considering mobile computing
devices as user interface to the system connecting mass people
database with cloud computing environment resources. The
causes of education termination are investigated by analyzing the
sample database in terms of attribute value relationship in the
form of association rules to reason about the causes based on the
computed support and confidence. It is observed that if the
affected family had no service holders, the dropped student had
to stop his education because of financial problem. Classification
is applied to classify the dropped students in different groups
based on their level of education.
Keywords-database; data mining; knowledge extraction;
decision making; development policy making
I. INTRODUCTION
In recent days, massive data are collected through
customized application software operating various
organizations. It is infeasible to extract knowledge from
millions of data records which are stored using various
RDBMS tools, e.g., Oracle, MySQL etc. manually for using in
decision making. Various data mining tools e.g., Weka,
DBMiner, Oracle Miner are available for mining knowledge
from databases for easier and efficient decision making.
Android-based mobile devices are massively used to access
web-based applications. Data about the personal quality and
activities of the mass people can be collected through web
applications. Intelligent applications can be developed to be
executed on application servers to access the mass people
database to analyze data and extract knowledge to assist in
decision making on their doings to improve their life standard.
To build a knowledge-based developed society, the people’s
activities may be monitored and guided based on their
personal information and daily activities to help and suggest
accordingly as required to ensure development [1].
Data mining is the extraction of hidden knowledge or
interesting patterns from databases or data warehouses.
Various methods e.g., classification, association rule analysis
and clustering can be applied to the database for the extraction
of hidden knowledge or interesting patterns. In this research,
the application of association rules [2]-[7] and classification
technique [8]-[10] have been applied on a sample database to
extract knowledge to aid decision making [11]. The use of
association rules in classification purpose is presented in [9],
[10]. One of the objectives of this research is to identify and
define the real world problem using data mining concepts so
that domain knowledge can be extracted using data mining
technique to aid decision making. The other objective is to
investigate the design of a framework integrating mass people
database, mobile computing devices and data mining system
within cloud computing environment [12]-[14] for knowledge-
based development policy making [1]. This paper presents the
steps toward development policy making by providing simple
examples. The details of the development policy making is out
of scope of this paper.
The paper is organized as follows. Section II describes
development policy making. Section III explains how data
mining technique can be applied to extract knowledge for
decision making defining an example problem. Section IV
presents the methodology defining the various stages of
knowledge extraction and decision making for development
policy making. Section V presents the proposed system
architecture. Section VI provides experimentation and results
using a data mining tool called Weka on a sample database to
extract knowledge for decision making. Finally, section VII
concludes and gives a guideline for future work.
II. DEVELOPMENT POLICY MAKING
Any development in personal or organizational level may
be achieved by conducting some development activities for a
period of time systematically. The deviation from the expected
development in personal and organizational level may be
traced using peoples and organizational databases. This may
be monitored to try to construct successful life and
organizational success by guiding the activities using
intelligent human guidance and automatic intelligent devices
executing intelligent software systems [1]. Android-based
mobile devices can be used as an interface to access web-
based applications connecting cloud database and application
The research work presented in [1] was funded by the University Grants
Commission (UGC) of Bangladesh in 2014-2015 in the Faculty of
Mathematical and Physical Sciences, Jahangirnagar University, Savar, Dhaka,
Bangladesh.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 3, March 2018
174 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
2. servers to get big database storage and computing support.
These cloud database servers can be used to store data about
the personal quality and activities of the people collected
through various applications. For the proper running and faster
development of any organization, better policies must be
formulated, and need to be employed in its operation. Domain
knowledge, operational data as well as contextual information
play vital role in making successful policies for organizations.
Business data collection and automatic processing of data
using intelligent software systems may enable knowledge
extraction from the collected data to aid decision making.
Appropriate decisions may be taken and policies may be
formulated based on the extracted knowledge considering the
contextual factors. In this paper, three terms: i) domain
knowledge ii) decision making and iii) development policy are
key terms. The development policy making involves: i)
Specifying the problem describing the intended development
ii) Finding a solution involving decision making using the
domain knowledge with identifying the development activities
for policy formulation to solve the problem to achieve the
expected development, and iii) Building an overall plan to
implement the policy. Tracking progress in the domain is also
required to monitor the success of the development policy.
III. DEFINING DATA MINING PROBLEM FOR DECISION
MAKING
A social problem is stated below to clarify a real world
system problem for automation and knowledge extraction for
decision making to fix and formulate the appropriate
development policy.
Example 1. A school going boy of a rural area is not going
to his primary school for a month. An undergraduate student
of that village noticed this. The fact is due to the shortage of
money because of being a family member of a very low
income group. So, he was unable to continue his school
education because of being busy with money earning job to
bear the family expenses. Such students can be identified of an
area and their family properties can be analyzed using data
mining techniques to identify the reason of termination of
school education of the dropped students to provide any form
of financial help to continue their school education.
In Example 1, a problem is stated which clarifies that a
school going boy has to stop his school education because of
being busy with money earning job to support his family
expenses. In a region, various data about such school going
boys who have to stop their school education can be collected
through web-based applications using mobile devices or any
computers where Internet facility is available. The collected
data can be stored in database servers for further processing to
extract knowledge about the problem to reason about the cause
of termination to help decision making to support their further
school education. Finding and allocating some sort of financial
help to the affected students is urgent to continue their
education. Any development policy to support funding the
school education of the affected students may include granting
any form of financial support to the students who are unable to
continue their school education.
The solution to the above problem requires data collection
about the school dropped students as described in the problem
statement of Example 1 of a region, data analysis and
knowledge extraction from the collected data of the affected
students. A solution may be provided concerning decisions and
a policy formulation based on the extracted knowledge. The
design of a framework for making any development policy
need to include a data mining system for knowledge extraction
from the domain database for using in decision making [1].
Data mining technique can be applied to extract knowledge
from this data for using in decision making [11]. Millions of
data records are collected from the daily operations in various
organizations, e.g., super shops, education sector, hospitals,
business organizations, and many other sectors. The
Management Information System (MIS) and Decision Support
System (DSS) personnel of the organizations make use of the
knowledge or patterns hidden in this massive data in decision
making. The real problem is to identify the patterns, rules and
models, and the extraction of decision making knowledge from
the extracted patterns, rules and models. This requires the data
mining software systems to incorporate intelligent algorithms
to see insight into the data records, and discover patterns from
the data records to extract knowledge to use by the decision
making authority in making successful decisions to advice new
policy to make changes for improvement.
IV. METHODOLOGY
The decision making and policy formulation using the
extracted knowledge are two main activities of the knowledge-
based development policy making process. Some of the main
steps are explained below.
A. Identifying and Defining the Data Mining Problem for
Knowledge Extraction
In this step, the real world problem is identified and the
data mining problem statement is expressed to solve this
problem as described in Example 1. The problem statement
clarifies the domain problem and specifies what sort of
knowledge is to be extracted from the domain data for
particular decision making. It should also provide a hints in
the formulation of the development policy. Considering
Example 1, this step should specify the frequent causes which
force the students to leave their schools.
B. Preparing Sample Data
This step includes the sample data preparation activities.
Data may be collected through customized application
software directly into databases stored in database servers
using database management system (DBMS) tools, e.g.,
MySQL, Oracle or data can be manually collected to store into
spreadsheets or databases using DBMS tools for further
processing. In this research work, a sample database of some
school dropped students is stored into a spreadsheet for
processing using Weka data mining tool. Most of them are of
current age 15 to 30 years.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 3, March 2018
175 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
3. TABLE I represents the properties about the school
dropped students to reason about the termination causes of
their school education at an early age. The sample database
can be used to extract knowledge applying data mining
technique for using in decision making.
C. Choosing the Knowledge Extraction Methods
Various data mining methods e.g., classification,
association rule analysis and clustering are used for knowledge
extraction from databases and data warehouses. A database
created for a real world domain, e.g., Banking System, Super
Shop Sales System may contain various patterns of data. Data
may need to be organized in groups to apply the appropriate
data mining method to extract knowledge. For example,
classification method may be applicable to a particular group
of data while association rule mining may not be applicable to
that group of data.
D. Specifying the Decision Making Knowledge to be
Extracted
The output obtained by applying the various intelligent
algorithms employed in data mining methods on the training
data set can be represented in the form of association rules,
decision trees and neural networks in terms of existing
attribute values, test conditions, predicted values, and
constraints. Data mining is usually performed on a single
relation, though multi-relational data mining methods can also
be employed. In Example 1, the knowledge required for
decision making can be of the following forms:
Sub-Problem 1: To know about the termination reasons of
school education of the dropped students.
Sub-Problem 2: To classify the school dropped students
based on their last education at which they had to stop their
education.
The knowledge required for Sub-Problem 1 can be
specified by mining association rules to represent the
association relation among the attribute values using the
sample data by applying the association rule generation
algorithm and computing their support and confidence. The
knowledge required for Sub-Problem 2 can be specified by
building a classification model from the sample data using
classification algorithms [8]-[10] by constructing decision
trees.
E. Extraction of Knowledge
Appropriate data mining methods implementing intelligent
data mining algorithms [2]-[5], [8]-[10] can be applied on the
database for knowledge extraction. Various data mining tools
e.g., Weka, Neuralware, DBMiner, Rapid miner are available
which can be applied on the database to extract knowledge.
Customized applications can be developed implementing
intelligent algorithms to mine knowledge and patterns from
personal and organizational databases, texts and web pages in
the form of decision trees, neural networks, association rules
with their support and confidence, if-then rules, clusters and so
on.
F. Decision Making Using the Extracted Knowledge
Making appropriate decisions at the right time in
organizational policy making is crucial for organizational
success. Interesting patterns and knowledge extracted from the
organizational database storing data in countrywide various
sectors e.g., agriculture, education, law and discipline, mass
people activities, environment and business organizations can
be used by the decision making body of the organizations to
make strategic, managerial and operational decisions [1] for
organizational development. Knowledge extraction from
personal data using data mining technique may help
development policy making at personal level. Decisions
should be made based on the extracted knowledge to decide
what should be done or not. In this paper, two approaches-
association rule mining [2]-[5] and classification [8]-[10] have
been applied on sample database to generate association rules
and build decision trees. The support and confidence of each
of the association rules and the decision tree model can be
used in decision making [11], [15]-[17]. The extracted
knowledge helps optimization in decision making [17]
process.
To clarify decision making, we consider a super shop sales
TABLE I. ATTRIBUTE DESCRIPTION [1]
Attribute Meaning
Person Person Identifier
Gender Represents whether Male or Female
Age Current age
LastclassStudied The last class in which the student left his school
ReasonofStudyTermination The main cause of study termination
FamilyEducation Represents that the dropped student’s family has any other educated member or not
ServiceHolders Represents that the dropped student’s family has any service holder or not
Other_Income_Source Represents that the dropped student’s family has any other income source except service or not
Last_Education Level of the last education
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 3, March 2018
176 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
4. system. If the support and confidence both are above 50% for
the current purchase of the customer Z, which contains item Y
or items (X,Y), then the customer Z is a frequent buyer of item
Y or items (X,Y), and he may get a discount on item Y or
items (X,Y). This may inspire the customers to buy these
items at a regular basis, which may increase the sales of the
super shop to earn more profit. This may cause higher amount
of purchase orders to the suppliers to increase the stock.
G. Formulating the Development Policy
A development policy needs to execute many decisions
during the implementation of the policy where the decisions
are made based on the extracted knowledge. The formulation
of a policy involves building a plan consisting of a set of
actions to reflect the decisions to achieve some form of
development. Upon executing the actions during the life span
of the policy, the specific policy is implemented and it is
expected that some development can be achieved.
H. Justifying the Correctness of the Proposed Approach
The success of an organization depends on the use of
domain knowledge in efficient decision making which is
actually done by applying human skill, labor, expertise and
intelligence in most of the cases. The knowledge required for
decision making can be extracted by applying intelligent data
mining algorithms on the domain database for easier and faster
decision making which will speed up the decision making
process. Millions of records are collected through using
automated software systems and stored using DBMS tools,
which can’t be manually processed to extract knowledge. Data
mining algorithms are applied on the database after arranging
the attributes in a relation properly. The mined output is
evaluated using interestingness measures, e.g., support and
confidence for association rule analysis and hence the
extracted patterns and knowledge hopefully will be correct and
relevant for decision making. For the decision tree model
constructed using classification method, the model is built
using valid domain data, so the constructed model hopefully
will function correctly in decision making.
V. SYSTEM ARCHITECTURE
A system architecture shown in Fig. 1 is proposed
connecting target group of people using mobile computing
devices with cloud computing environment resources.
Fig. 1: Proposed System Architecture Connecting Computing Resources
within Cloud Computing Environment [1].
Each of the mobile devices and other computing devices in
Fig. 1 can be used to request a remote cloud server for a
service, and may get a service automatically being processed
within the cloud environment. The data mining system can be
applied on the database to extract knowledge for decision
making to reason about the domain problem. Web-based
applications can be developed to collect sample data from a
target group of people or any organization to store it directly
into the database servers. Cloud computing environment [12]-
[14] may provide the required resources for the solution of
massive data collection and processing by providing sharable
computing power and powerful database servers while running
intelligent software systems on application servers. The
security issues of the proposed system architecture are
considered of the standard security measures usually available
in cloud computing environment.
VI. EXPERIMENTATION AND RESULTS
Some sample data about some school dropped students are
analyzed using a data mining tool called Weka. The family
properties of the school dropped students are stored in a
spreadsheet database. TABLE I defines the meaning of 9
attributes of the sample database, and TABLE II summarizes
the attributes and their corresponding values. Two methods-
association rule mining and classification have been applied
on the sample database which are described below. The data
items must be organized emphasizing the causes of
Mobile devices and other computing device as
interface to cloud resources
People database, Application Programs, Computing devices,
Database servers, Data centers, BI tools, Data mining and
data warehousing systems within Cloud computing
environment
Cloud Resources provided by Google, Yahoo
and other organizations
Mobile computing devices and other computing devices
receive policy information from cloud computing environment
TABLE II. ATTRIBUTE VALUES [1]
Attribute Values
Person {M1, M2, M3, M4, M5, M6, M7, M8, M9, M10, M11, M12, M13, F1, F2, F3, F4, F5, F6}
Gender {M, F}
LastclassStudied {III, IV, V, VI, VIII, IX}
ReasonofStudyTermination {Early Marriage, Financial Problem}
FamilyEducation {Yes, No}
ServiceHolders {Yes, No}
Other_Income_Source {Poor Agriculture, Middle class Agriculture, No Fixed Source, Poor Business}
Last_Education {Primary School, High School}
International Journal of Computer Science and Information Security (IJCSIS),
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5. termination of the school education of the dropped students in
organizing data relationships. Each data mining method is
applied to the sample database and the mined output is
analyzed for knowledge extraction to verify its use in decision
making.
A. Application of Association Rules in Decision Making for
Development Policy Making
An association rule [2]-[5] is an implication expression of
the form Χ Y where X and Y are antecedents and
consequents which are subsets of an item set W respectively,
and X Y = where is the implication operator. The
Weka 3.4.3 Associator1
is used to generate association rules
from the sample database containing 19 instances. The Age
attribute is removed from analysis as this attribute contains
continuous numeric values. Apriori is a famous algorithm [2]
for generating association rules from transactional databases.
A number of research works [3]-[5] have been carried out on
this algorithm in various applications. In this research work,
the data mining tool Weka is used to generate association rules
by applying Apriori algorithm using the attribute values of the
sample database. It is assumed that the Weka Associator will
be able to find association relationship among the attribute
values in the form of association rules to reason about the
causes of the termination. The strong rules consisting of the
most occurring attribute values can be used to reason about the
related domain facts.
The support s of an association rule Χ Y can be defined
as follows [2]-[5]:
Support, s(Χ Y) = (X Y) / N 100
where N is the number of records in the database.
The confidence c of an association rule Χ Y can be
defined as follows [2]-[5]:
Confidence, c(Χ Y) = (X Y) / (X) 100.
1
https://www.cs.waikato.ac.nz/ml/weka/
The best association rules generated using Weka 3.4.3
Associator using the sample database with minimum support =
0.4 and minimum confidence = 0.9 are shown in TABLE III.
Among the generated association rules, Rule 7 and Rule 9 are
rejected from analysis as these rules have no relevance to
reasoning. Other rules have also weak relevance to reasoning
though these rules need simplification by eliminating some
antecedent’s attribute-value relationships. Rule 1 is justified as
the most relevant rule for Example 1 and has the highest
support with sup = 14/19 × 100 = 73.68%, and confidence,
conf = 14/14 × 100 = 100% as shown in TABLE IV, which is
the most frequent association rule contained in TABLE III
with 14 occurrences of both antecedents and consequents
within the sample database. Rule 2 has also a good relevance
to the problem as stated in Example 1 with sup = 10/19 × 100
= 52.63% with conf = 10/10 × 100 = 100%.
TABLE IV. THE BEST ASSOCIATION RULE SELECTED FOR
REASONING
Rule No. sup (s%) conf (c%)
1 73.68 100.00
In Rule 1 as defined in TABLE III, the rule consequent is
the attribute ReasonofStudyTermination with the only value
Financial Problem. Rule 1 expresses that the reason of their
termination of the school education is Financial Problem with
no service holders in the family to earn money. A decision can
be made to provide any sort of financial support to the affected
students by the proper authority if the problem is identified at
the right time. Hence, a development policy by the proper
authority may need to include the decision to support the
continuation of such school dropped students by providing any
financial support if the termination causes can be identified
when the problem occur.
TABLE III. THE BEST ASSOCIATION RULES [1] GENERATED USING WEKA 3.4.3
Rule No. Rule
1. ServiceHolders=No 14 ==> ReasonofStudyTermination=Financial_Problem 14 conf:(1)
2. FamilyEducation=No ServiceHolders=No 10 ==> ReasonofStudyTermination=Financial_Problem 10 conf:(1)
3. ServiceHolders=No Last_Education=Primary_School 10 ReasonofStudyTermination=Financial_Problem 10 conf:(1)
4. Other_Income_Source=Poor_Agriculture 9 ==> ReasonofStudyTermination=Financial_Problem 9 conf:(1)
5. Gender=M ServiceHolders=No 9 ==> ReasonofStudyTermination=Financial_Problem 9 conf:(1)
6. FamilyEducation=No ServiceHolders=No Last_Education=Primary_School 8 ==> ReasonofStudyTermination=Financial_Problem 8 conf:(1)
7. Gender=M 13 ==> ReasonofStudyTermination=Financial_Problem 12 conf:(0.92)
8. FamilyEducation=No 13 ==> ReasonofStudyTermination=Financial_Problem 12 conf:(0.92)
9. Last_Education=Primary_School 13 ==> ReasonofStudyTermination=Financial_Problem 12 conf:(0.92)
10. FamilyEducation=No Last_Education=Primary_School 11 ==> ReasonofStudyTermination=Financial_Problem 10 conf:(0.91)
International Journal of Computer Science and Information Security (IJCSIS),
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6. B. Application of Classification Method in Decision Making
for Development Policy Making
In data mining, classification method [8]-[10], [18], [19]
can be applied on a set of training data records using several
algorithms e.g, ID3, C4.5 to build a decision tree model. The
decision tree model is applied on a set of test data to verify its
accuracy, and later it can be used for classification. Fig. 3
shows the decision tree obtained by using Weka 3.7.12 2
classify module by applying J48 algorithm on the sample
database. By replacing the attribute value High School with
HS and Primary School with PS for the column
Last_Education within the rows of sample database, the
application of Weka 3.7.12 classify module with J48
classification algorithm on the resulting database constructed
the decision tree shown in Fig. 4. By summarizing the data
shown in Fig. 3 and Fig. 4, a more meaningful information can
be provided as shown in TABLE V, which is graphically
plotted in Fig. 5.
Fig. 3: Classification Using Weka 3.7.12 Classify Module Using the
Sample Database.
Fig. 4: Classification Using Weka 3.7.12 Classifier Using the Modified
Database Obtained from the Sample Database.
TABLE V. NUMBER OF DROPPED STUDENTS IN EACH CLASS MOST
ARE OF CURRENT AGE 15-30 YEARS
LastclassStudied
Last_
Education
Number of Dropped
Students
III Primary School 2
IV Primary School 5
V Primary School 6
VI High School 2
VIII High School 1
IX High School 3
2
https://www.cs.waikato.ac.nz/ml/weka/
0
1
2
3
4
5
6
7
Primary
School
Primary
School
Primary
School
High
School
High
School
High
School
III IV V VI VIII IX
Number of Dropped Students in Each Class
Fig. 5: Number of Dropped Students in Each Class Most are of Current
Age 15-30 Years.
The information and knowledge provided in TABLE V and
Fig. 5 can be used in the formulation of a development policy
for improving child education and should emphasis on
reducing the drop of students at primary school education level
more than high school education level as more number of
students are dropped at primary school level. The affected
students can be supported by developing an education policy
called Child Education Policy for the Poor Children to provide
any form of financial support.
VII. CONCLUSION AND FUTURE WORK
In this paper, the application of two data mining methods
has been investigated to extract knowledge from the sample
database of some school dropped students to use in decision
making for development policy making. An architecture for
knowledge extraction to aid development policy making has
been presented. Attribute value relationship has been analyzed
using association rules generated from the sample database
using Weka data mining tool. The attribute value relationships
are analyzed by computing the support and confidence of the
rules to reason about the causes of termination of the school
education using the extracted knowledge. It is a novel
application of association rules in reasoning about facts.
Classification technique has been applied on the sample
database to construct a decision tree model which represents
the category of the dropped students based on their last
education at which they had to stop their school education. To
make more efficient use of information and knowledge
represented by the decision tree model, the data and
information are extracted and summarized using Table and
chart for efficient decision making. The data collection about
the dropped students through online systems at real time may
help to achieve effective solution using the extracted
knowledge by the proper authority to formulate a development
policy to provide financial assistance to these students so that
they can continue their school education. In future, data mining
application software may be developed to integrate with web-
based on-line customized applications to aid knowledge-based
efficient decision making in order to speed up successful
International Journal of Computer Science and Information Security (IJCSIS),
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179 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
7. development policy formulation to automate social problem
solution.
ACKNOWLEDGMENT
The author would like to thank the experts who made
valuable comments to this research work. He also thanks all of
the people who helped him in any matter in relation to this
research work.
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AUTHOR PROFILE
Md. Humayun Kabir received his Doctor of Philosophy degree in the area of
formal software development from the School of Computing, Dublin City
University in 2007, Ireland. He received Master of Science degree in
Computer Science from Dhaka University in 1992, and Bachelor of Science
(Hons.) degree in Applied Physics and Electronics from Dhaka University in
1991, Bangladesh. He is currently working as a Professor in the Department
of Computer Science and Engineering, Jahangirnagar University, Savar,
Dhaka. His research interests include Data mining, program transformation,
automatic program construction, software architecture and so on.
International Journal of Computer Science and Information Security (IJCSIS),
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