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We propose a data mining approach to predict the wine's quality level in order to improve the quality of products for wine enterprises in this paper. A large dataset is considered and three regression techniques were applied. Through the comparison, we get the conclusion that the model established by neural network is more accurate and it can improve the quality of wine's production.
Based on the real data of a Chinese commercial bank's credit card, in this paper, we classify the credit card customers into four classifications by K-means. Then we built forecasting models separately based on four data mining methods such as C5.0, neural network, chi-squared automatic interaction detector, and classification and regression tree according to the background information of the credit...
Wind power is one of the most rapidly growing renewable energies for power generation nowadays. However, operation of power systems becomes challenging due to intermittent characteristics from wind energies. Consequently, effective wind power forecasting is crucial because of the economic consideration and operation. This paper presents a novel technique for short-term wind power and wind speed forecasting...
This paper uses GMDH method to establish a prediction model to forecast the output value of transport & storage of Guangdong in China, since the original samples of the output value of transport & storage are less enough to be used with the traditional methods. Compared with traditional linear regression and artificial neural network, the predicted results show that GMDH method is an effective...
Since the current fashion color forecasts have some disadvantages in practical application, there is considerable interest in building models that can predict fashion value of the colors precisely and swiftly from historical data. This paper proposed a new forecasting model called G-LMBPNN (Gray Levenberg-Marquardt Back Propagation Neural Network). It utilizes gray process to obscure the data sequence...
Data mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous...
Application of the rough set theory and BP neural network model in disease diagnosis is discussed in this paper. BP neural network model was established, and trained by the real diagnosis data of nephritis, utilizing the neural network toolbox in Matlab software. In this way we were able to provide a good solution to the problem of diagnose for new patients based on their chemical test data. By data...
This paper use Microsoft SQL Server 2005 data mining tools and three methods of neural networks, decision trees and logistic regression to establish the financial crisis early-warning model of listed companies. The conclusion is that the three kinds of methods have good results and the prediction accuracy rate are 80% or more. The accuracy of the decision tree algorithm model is higher than others.
This paper presents a neural-network-based active learning procedure for computer network intrusion detection. Applying data mining and machine learning techniques to network intrusion detection often faces the problem of very large training dataset size. For example, the training dataset commonly used for the DARPA KDD-1999 offline intrusion detection project contained approximately five hundred...
This article is based on Data mining technology how to apply in the personal credit. Using decision tree algorithm, supporting data processing methods and more potential information for firms in order to facilitate business-to-customer to take a different credit programs.
To model market dynamics is a challenge that has attracted the interest of practitioners and researchers alike. This problem has been addressed from the perspective of Game Theory, in models that explicitly include profit-maximization schemes for the companies, and also from the point of view of Data Mining, with models that consider multivariate functions to model customer demands and related phenomena...
This paper applies DEA model to a sample of 58 power plate listed companies in the securities market in China in 2008, with a view to identifying the financial risk companies and non-financial risk companies, instead of using ST in the past. Then, after comparing logit regression model and neural network LVQ in predicting the company financial risks, the conclusion was drawn that neural network LVQ...
Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by...
In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier...
In this paper, using factor analysis to study the sources of China's financial risk, concluded that the major factor of the financial risks is macroeconomic risk, foreign investment risk, banking risk and the stock market risk; using BP artificial neural network model for the establishment of early warning and training and testing the sample data with it, and then prediction the state of the financial...
The large-scale and super-strength development of mineral resources in mining concentrated area in long term has made great contributions to Chinese economic construction and development, but it has caused serious damage to the ecological environment even ecological imbalance at the same time. In this study, according to the characteristics of mining concentrated area, the scientific and practical...
The aim of this paper is to describe an alternative analytical method in order to evaluate customer outage cost (COC) in Thailand. The information of electrical expense, outage frequency, outage duration, and process recovery time from industrial customers is gathered. They are used to be inputs of the proposed adaptive neuro fuzzy inference system (ANFIS). In the data training by neural network,...
Data classification is a prime task in data mining. Accurate and simple data classification task can help the clustering of large dataset appropriately. In this paper we have experimented and suggested a simple ANN based classification models called as minimal ANN (MANN) for different classification problems. The GA is used for optimally finding out the number of neurons in the single hidden layered...
In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier...
Mine work face gas emission is the important basis for mine design, and has important practical significance for ventilation and safety production. Between mine gas emission and work face there are complex nonlinear relationships. The paper constructed a work face gas emission prediction model based on wavelet neural network. It based on statistics of a mine work face gas emission data, applied the...
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