Data Mining Based Intelligent System for Voting Behavior Analysis

2013 ◽  
Vol 284-287 ◽  
pp. 3070-3073
Author(s):  
Duen Kai Chen
Keyword(s):  
Data Mining ◽  
Behavior Analysis ◽  
Voting Behavior ◽  
Intelligent System ◽  
Data Sets ◽  
Tree Model ◽  
Mining Technology ◽  
Identification Rate ◽  
Voter Identification ◽  
Election Studies

In this study, we report a voting behavior analysis intelligent system based on data mining technology. From previous literature, we have witnessed increasing number of studies applied information technology to facilitate voting behavior analysis. In this study, we built a likely voter identification model through the use of data mining technology, the classification algorithm used here constructs decision tree model to identify voters and non voters. This model is evaluated by its accuracy and number of attributes used to correctly identify likely voter. Our goal is to try to use just a small number of survey questions while maintaining the accuracy rates of other similar models. This model was built and tested on Taiwan’s Election and Democratization Study (TEDS) data sets. According to the experimental results, the proposed model can improve likely voter identification rate and this finding is consistent with previous studies based on American National Election Studies.

Finding Persistent Strong Rules

2010 ◽  
pp. 85-107
Author(s):  
Anthony Scime ◽  
Karthik Rajasethupathy ◽  
Kulathur S. Rajasethupathy ◽  
Gregg R. Murray
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strong Association ◽  
National Election ◽  
Data Sets ◽  
Rule Discovery ◽  
Discovery Process ◽  
Data Set ◽  
Rule Sets ◽  
Election Studies

Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.

Finding Persistent Strong Rules

Data Mining ◽  
2013 ◽  
pp. 28-49
Author(s):  
Anthony Scime ◽  
Karthik Rajasethupathy ◽  
Kulathur S. Rajasethupathy ◽  
Gregg R. Murray
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strong Association ◽  
National Election ◽  
Data Sets ◽  
Rule Discovery ◽  
Discovery Process ◽  
Data Set ◽  
Rule Sets ◽  
Election Studies

Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.

scholarly journals Predicting Paediatric Malaria Occurrence Using Classification Algorithm in Data Mining

2019 ◽  
pp. 1-10 ◽  
Author(s):  
T. C. Olayinka ◽  
S. C. Chiemeke
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Prediction Method ◽  
Developed Countries ◽  
Classification Algorithms ◽  
Data Sets ◽  
Tree Model ◽  
Healthcare Data ◽  
Paediatric Malaria ◽  
The Developed Countries

This paper gives the current overview of the application of data mining techniques on the haematological and biochemical dataset to predict the occurrence of malaria in children between age zero (0) and five (5).  Malaria has been eradicated from the developed countries but still affecting a large part of the world negatively. A larger percentage of malaria is estimated to affect young children in sub-Sahara Africa.  In order to reduce mortality from paediatric malaria, there should be an efficient and effective prediction method.  In healthcare, data mining is one of the most vital and motivating areas of research with the objective of finding meaningful information from huge data sets and provides an efficient analytical approach for detecting unknown and valuable information in healthcare data.  In this study, a model was built to predict the occurrence of malaria in children between age zero (0) and five (5) years, using decision tree classification algorithms on WEKA workbench tool.  The classification algorithms used are LMT, REPTree, Hoeffding tree and J48. A J48 algorithm was used for building the decision tree model since it has higher accuracy for performance with least error margin.

RESEARCH AND APPLICATION OF GIS AND DATA MINING TECHNOLOGY IN MONITORING AND ASSESSMENT OF NATURAL GEOGRAPHY ENVIRONMENT X

2018 ◽  
Vol 48 (4) ◽  
pp. 299-304
Author(s):  
X. L. ZHENG
Keyword(s):  
Data Mining ◽  
Development Process ◽  
Monitoring And Evaluation ◽  
Ecological Environment ◽  
Environment Monitoring ◽  
Gis Technology ◽  
Tree Model ◽  
Mining Technology ◽  
Geographical Environment ◽  
Land Types

At present, with the acceleration of the economic development process, the maintenance of the ecological environment has received extensive attention. In order to simplify the workflow of natural geographical environment monitoring and evaluation, this paper combines GIS technology and data mining technology, and builds a decision tree model with monitoring and evaluation as the core. Dongting Lake is taken as the research object to verify the validity of the model. The research results show that the algorithm designed in this paper can classify the land types of natural geographical environment and improve the accuracy of environmental monitoring and evaluation.

scholarly journals Design and Implementation of Hybrid Algorithm for e-news Classification.

2013 ◽  
Vol 12 (1) ◽  
pp. 3178-3186
Author(s):  
Harneet Kaur ◽  
Kiran Jyoti
Keyword(s):  
Data Mining ◽  
Hybrid Algorithm ◽  
Intelligent System ◽  
Large Data ◽  
Online News ◽  
Data Sets ◽  
Online Newspapers ◽  
Use Of Data ◽  
Internet News ◽  
Use Of Internet

Data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. As the use of internet is increasing day by day and with the advancement of internet news also publish online. So to handle this bulk amount of news various data mining techniques for classification had been used. In this paper we are using an intelligent system based on Hybrid algorithm (HMM, SVM and CART) for e-news classification. An intelligent system is designed which will extract the online news and then will find out category and subcategory wise news. System involves four main stages: a) Keyword Extraction b) Implementation of Hybrid Algorithm (HMM, SVM and CART). Data have been collected for experimentation from online newspapers like The Hindu, Hindustan Times and Times of India. The experimental results are based on the news categories and sub categories such as Entertainment: Bollywood 100% and Hollywood 90%, Sports: Cricket 90%, Football 90% and Hockey 78%, Matrimonial :Hindu 100% and Muslim 80%. In this paper we also compare the result of Hybrid algorithm (HMM, SVM and CART) with individual HMM and SVM Algorithm and conclude that Hybrid algorithm (HMM, SVM and CART) gave better result than that of what HMM and SVM individually gave.

scholarly journals Analysis for Clients Churn of Credit Cards in Model Construction in Banking Industry

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Jianyao Liu
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Financial Market ◽  
Banking Industry ◽  
Credit Cards ◽  
Training Data ◽  
Tree Model ◽  
Data Set ◽  
Mining Technology ◽  
Testing Data

Data mining technology has been more and more important in the economics and financial market. Helping the banks to predict a customers’ behavior, which is that whether the existing customers will continue use their credit cards or not, we utilize the data mining technology to construct a convenient and effective model, Decision Tree. By using our Decision Tree model, which can classify the customers according to different features step by step, the banks are able to predict the customers’ behavior well. The main steps of our experiment includes collecting statistics from the bank, utilizing Min-Max normalization to preprocess the data set, employing the training data set to construct our model, examining the model by testing data set, and analyzing the results.

scholarly journals The Impact of Entrepreneurship Education of Entrepreneurs on the Entrepreneurial Psychology of Sports Majors From the Perspective of Pedagogy

2021 ◽  
Vol 12 ◽  
Author(s):  
Taofeng Liu ◽  
Mariusz Lipowski ◽  
Yingying Xue ◽  
Tao Xiao ◽  
Hongzhen Liu ◽  
...  
Keyword(s):  
Data Mining ◽  
Influencing Factors ◽  
Entrepreneurship Education ◽  
Behavior Control ◽  
Entrepreneurial Behavior ◽  
Tree Model ◽  
Mining Technology ◽  
Sports Industry ◽  
National Training ◽  
The Impact

In recent years, with the continuous reform and innovation of the sports industry, the national training of sports talents has gradually developed into the training mode of skilled sports talents and professional talents in the field of sports. Therefore, the research on the influence of entrepreneurship education on the entrepreneurial psychology of sports majors has become the inevitable requirement of the development of the sports industry. The purposes are to understand the entrepreneurial psychology and its influencing factors of the students in sports majors after graduation and promote more suitable college students to start businesses and realize self-value. With the students in sports majors in four colleges of Y province as the research object, the typical model in psychology, planning behavior model, is taken as the basic theoretical basis. The questionnaire method combined with the data mining technology based on the decision tree model is adopted to study the influencing factors of entrepreneurial psychology of sports majors. It focuses on the influencing factors and mechanisms of the entrepreneurial drive of sports students. The results show that the three factors, namely, entrepreneurial behavior attitude, entrepreneurial subjective norms, and entrepreneurial perceptual behavior control, are different and interrelated. They are inseparable and can be transformed into each other under certain conditions. Three factors jointly drive the entrepreneurial behavior of students in sports majors. The entrepreneurial drive of students in sports majors in Y province is a dynamic system mechanism, which is analyzed using data mining technology. The entrepreneurial perceptual behavior control is the core factor affecting the entrepreneurial drive of students in sports majors. However, the success rate of entrepreneurs will be higher when the three elements play a reasonable role. The subjective factors driving their entrepreneurship will be reduced in direct proportion when entrepreneurs are deficient in one aspect.

scholarly journals Research on college English teaching based on data mining technology

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jinhui Duan ◽  
Rui Gao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Conversion ◽  
Tree Model ◽  
Mining Technology ◽  
Processing Technologies ◽  
College English ◽  
English Teaching ◽  
School Teaching

AbstractTo improve the efficiency and quality of college English teaching, we analyzed the feasibility and application process of data mining technology in college English teaching. The entire process of data classification mining was fully realized. A new teaching program was proposed. The object and target of data mining were determined. Online surveys were used to collect data. Data integration, data cleaning, data conversion, data reduction and other pre-processing technologies were adopted. The decision tree was generated by using the C4.5 algorithm, and the pruning was carried out. The result analysis decision tree model was completed. A detailed survey of the students' English learning in University was made in detail. The results showed that the qualified rate of students' English performance was increased from 20–30% to 50–60%. Therefore, the classification rules provide theoretical support for the school teaching decision. This method can improve the quality of English teaching.

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