The Searching of Decision Tree Based on Data Mining

2013 ◽  
Vol 380-384 ◽  
pp. 2633-2636 ◽  
Author(s):  
Yu Ling Ma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Information Technology ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Set Theory ◽  
Association Rules ◽  
Mining Technology ◽  
Artificial Neural

With the promotion of information technology in various fields, the amount of data grow crazily, how to find what we need in the information ocean is a problem to be solved, and the data mining technology has made the issue to be resolved. This paper introduced the data mining knowledge briefly, for example: set theory, decision tree, clustering and association rules, artificial neural network, genetic algorithm and so on, then analysis the method of decision tree in detail by example.

Data mining of digital phytosanitary

2021 ◽  
Author(s):  
T. Z. Ibragimov ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Bayesian Classifier ◽  
Design Parameters ◽  
Data Set ◽  
Septoria Leaf Blotch ◽  
Leaf Blotch ◽  
Artificial Neural

methods of data mining were used to predict the Septoria leaf blotch of wheat. A system has been developed that allows parallel forecasting with the same data set using the methods of an artificial neural network, a decision tree, and a naive Bayesian classifier. The system allows you to interactively adjust the design parameters for each of the methods, see the results obtained and evaluate their effectiveness.

The Prediction of River Water Pollution Density Based on Data Mining Technology

2010 ◽  
Vol 113-116 ◽  
pp. 1285-1288
Author(s):  
Chen Ye Wang ◽  
Bin Sheng Liu ◽  
Er Wei Qiu
Keyword(s):  
Neural Network ◽  
Water Pollution ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Rough Set Theory ◽  
Mining Technology ◽  
Bp Artificial Neural Network ◽  
Three Stages ◽  
Artificial Neural ◽  
Using Data

In order to increase the prediction precision, this article proposes a forecasting model in water pollution density based on data mining technology. The model consists of three stages: first, the rough set theory and the genetic algorithm are applied to select relevant forecasting variable to the water pollution density; second, training pattern of artificial neural network which is similar to the forecast term is carried out by using data mining technology; finally the artificial neural network is used to carry on forecasting the water pollution density. The applied result shows that this model has a higher precision and surpasses gray GM (1, 1) and the pure BP artificial neural network model.

Construction Financial Crisis Warning Model Using Data Mining

2011 ◽  
Vol 271-273 ◽  
pp. 684-688
Author(s):  
Nai Hsin Pan ◽  
Ming Li Lee ◽  
Chia Wei Chang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Financial Crisis ◽  
Decision Tree ◽  
Earnings Per Share ◽  
Forecasting Performance ◽  
Equity Ratio ◽  
Artificial Neural ◽  
Warning Model

This paper employs artificial neural network of data mining and decision tree algorithm to build financial crisis warning model. The research results show that, forecasting performance of artificial neural network is better than that of decision tree model, hence, “financial statement average warning model” established through artificial neural network based on the average revenue of the past three years before financial crisis has better forecasting performance than the “annual report forecast model”. Factor analysis is employed to select common factor in 1 year before financial crisis, and the critical variables of financial crisis are found to be: debt-to-equity ratio, quick ratio, borrowing dependence, inventory turnover ratio, and earnings per share. According to the decision tree rule, variables differentiable to financial crisis warning are debt-to-equity ratio, earnings per share, and borrowing dependence.

Sign in / Sign up

Export Citation Format

Share Document