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The fuzzy neural network technology is one of the hot topics of Data Mining. According to the Max Similarity Rule, this paper sets forth the cross entropy theory with formulae deduction in detail and a new activation function. Compare with the BP algorithm (error back propagation), which based on the error square sum rule and Sigmoid or Hyperbolical function, the classify algorithm based on the cross...
Auditing practices nowadays have to cope with an increasing number of fraudulent financial statements. Data mining techniques can facilitate auditors in accomplishing the task of detection of fraudulent financial statements (FFS). Considering the character of FFS, this paper designs a FFS detection model based on Naïve Bayes classifier. To perform the experiment, we choose 44 FFS according to the...
There are deficits in the current operating analysis system of telecommunication basic operator. Accordingly, this paper proposed a new system based on data warehouse, which was using data mining. This article constructed the overall framework, described the whole function of the system, and especially detailed in the main function.
Customers are resources of the enterprises' profits, Customer satisfaction degree is defined as a measure of how a firm's product or service performs compared to customer's expectations. With the market developing quickly, how to improve the customers' satisfaction degree has become the main task and object for one company. It has been a subject of research due to its importance for measuring marketing...
An effect and efficient response to the complaints from customer is an essential indicator of a service-oriented company's performance, especially for a high-end restaurant chain group. Complaints reflect the needs for improvement for a restaurant, such as food taste, quality and quantity, speed of services, price, service attitude, decoration and environment factor, and sanitary factor. Customers...
So far, the K-means algorithm is the most widely used method for discovering clusters in data, and it has been used extensively in the commercial field, such as customer analysis. However, the efficiency of the algorithm needs to be improved when faced with large amounts of data. The improved algorithm avoids unnecessary calculations by using the triangle inequality. We applies the improved algorithm...
The paper introduces decision tree algorithm and C5.0 algorithm in the data mining at first. Then it introduces financial analysis methods, the problems which need to pay attention to in application and the selection process of attributes. At last, we study the financial ratios of listed logistics companies through the application of SPSS Clenmentine12.0 software. The accuracy of this model is as...
Searching on the Internet today can be compared to dragging a net across the surface of the ocean. While a great deal may be caught in the net, there is still a wealth of information that is deep, and therefore, missed. Deep Web sources store their content in searchable databases that only produce result dynamically in response to a direct request. In this paper, we proposed an automatic classification...
Nowadays, clustering algorithms are widely used in the commercial field, such as customer analysis, and this application has achieved good effect. K-means algorithm is by far the most commonly used method for clustering. Although, the time consumption is fairly high when faced with lager-scale data. In this paper, we improved the K-means algorithm. Our improvement is based on the triangle inequality...
The mining objects of traditional mining association rules techniques mainly focus on mono-database. With the rapid development of database technologies, multi-database mining is becoming more and more important. In order to make the synthetic result of multiple data sources more accurate, the parameter C, which indicates the number of transactions in local branch database, is proposed. We design...
At first, this paper introduces the CRM and data mining and the concept of Clustering Analysis, And then started from Customer relationship management's core values-Customer Value, deeply expound the meaning of customer value and it is in a key position in the customer relationship management. And in-depth study CRM Customer Value Analysis by applying Data Mining Technology, The purpose of in-depth...
E-commerce website accumulates a large number of customer reviews for merchandise and online shopping services. E-commerce enterprises and manufacturers could get customer opinion to improve service and merchandise through mining customer reviews. The paper presents a prototype system could be used to track and manage customer reviews, through mining topics and sentiment orientation from online customer...
Acquiring new customers in any business is much more expensive than trying to keep the existing ones. Many churn management models have been developed over the years, mostly focusing on prediction accuracy and not considering the range of parameters and processes essential to manage the system as a whole. This study presents CMF (churn management framework), a framework which tries to covers most...
Orthogonal defect classification (ODC) is a kind of defect analysis method invented by IBM. ODC classifies software defects by eight orthogonal attributes. By analyzing these attributes' distribution and increasing trend the software process information could be obtained. It has been used widely in many companies and organizations. In this paper, we focus on the ODC records collected in a company,...
Support vector machine recursive feature elimination (SVM-RFE) is a simple and efficient feature selection algorithm which has been used in many fields. Just like SVM itself, SVM-RFE was originally designed to solve binary feature selection problems. In this paper, we propose a new recursive feature elimination method based on SVM for ranking problem. As against standard approaches of treating ranking...
This paper suggests using FCM to investigate the problem of credit risk evaluation of listed companies by referring to some domestic and abroad researches of fuzzy cognitive map and credit risk. Firstly, the present research status of FCM is briefly introduced. Then, this paper completely studies the basis theory and inner inference mechanism of FCM, and the Active Hebbian Learning (AHL) algorithms...
Using the theory and method of unascertained measure, a novel unascertained C-means clustering model and the clustering weight are established. The basic knowledge of the unascertained sets and concept of unascertained clustering was introduced briefly. Then, the unascertained measure was defined and clustering weight were set up. Experimental results show that the presented algorithm performs more...
Dividend policy, part of the core issues of the corporate finance, always drawn close attention. Western corporations always follow the dividend theory and pay high stable cash dividends in order to promote the corporate value. In contrast, the dividends level in China is much lower, discontinuous and random. This is diametrically against to the western's dividends theory and practice. In this paper,...
This paper focuses on the development of knowledge model for a prediction of air cargo offload. A knowledge model is a model containing a set of knowledge via rules that has been obtained from mining certain amount of data. These rules might help the management in major decision making such as setting up new strategy. In this study, an intelligent technique for data mining called a rough set theory...
This paper discusses the possibility of adopting the concept of knowledge based systems [KBS], in general, and conceptual maps, in particular, in email classification system. Needless to say that email has the potential to improve efficiency and reduce costs involved in communication. Even after the advent of newer technologies such as instant messaging and VoIP, email remains the top most application...
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