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.

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.

Advances in Data Mining and Applications in Power Plants

2011 ◽  
Vol 347-353 ◽  
pp. 487-493
Author(s):  
Ang Bao ◽  
Wei Guo Pan ◽  
Wen Huan Wang
Keyword(s):  
Data Mining ◽  
Power Plants ◽  
Field Data ◽  
Thermal Power ◽  
Effective Means ◽  
Monitoring And Evaluation ◽  
Research Progress ◽  
Mining Technology ◽  
Equipment Operation ◽  
Real Time Database

Describes the theory and methods of data mining technology, and the latest research progress home and abroad. In the equipment operation of various thermal power plants, more and more field data is stored in the DCS real-time database, and there is always an abundance of knowledge hidden behind the data. Adopting the date mining technology to process and analyze these data can optimize the operation of power plants and provide effective means for monitoring and evaluation of the equipment.

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.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

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