Data Mining Technology Applications in Tobacco Commercial Enterprise

2012 ◽  
Vol 461 ◽  
pp. 418-420
Author(s):  
Yi Min Mo ◽  
Xin Shun Tong ◽  
Li Hua Yang

The wide application of information technology has greatly improve the work efficiency but also caused a large and complex data accumulation. How to get the valuable information from vast amounts of data are the key issues in data processing. This paper studied the application of data mining technology in tobacco commercial enterprise from three aspects: market demand forecasting, customer relationship management and historical data processing. Analysis of how to use data mining technology to make full use of large amounts of data to provide a basis for tobacco commercial enterprise’s decision-making.

2014 ◽  
Vol 945-949 ◽  
pp. 3360-3363
Author(s):  
Tian Song ◽  
Guang Jian Chen ◽  
Yu Mei Luo

Customer relationship management (CRM) focuses on the customer and also aims to reestablish the organizational structure, optimize the business procedure as well as carry out the research upon the customer so as to enhance the customer satisfaction degree and improve the efficiency and profit of the enterprise. The technology of data mining has provided the powerful technical support for the CRM. This paper will make an analysis on the thinking of CRM, the procedure of data mining as well as the application of data mining to the CRM.


2014 ◽  
Vol 513-517 ◽  
pp. 1940-1943
Author(s):  
Li Hong Yu ◽  
Ya Li Xu ◽  
Lin Dai

The computer data mining technology plays an important role in the financial risk management. It can extract the implicit data that people don't know in advance, in the mean time, and potentially useful information and knowledge for managers to provide decision-making reference. This paper introduces the concept of data mining, the process and main technology first, and then introduces the typical application of data mining in the financial risk management, such as customer relationship management, credit risk assessment and financial crisis early warning analysis. At last, it has a summary to provide the risk management for the financial industry.


2019 ◽  
Vol 23 (2) ◽  
pp. 42-49 ◽  
Author(s):  
K. V. Mulyukova ◽  
V. M. Kureichik

The purpose of the work is to study the current problems and prospects of the solution for processing big data received or stored in the Internet (web data), as well as the possibility of practical realization of Data Mining technology for big web data on practical example. Materials and methods. The study included a review of bibliographic sources on big data analysis problems.Data Mining technology was used to analyze large web data, as well as computer modeling of a practical problem using the C # programming language and creating a DDL database structure for accumulating web data.Results. In the course of the work, the specifics of big data were described, the main characteristics of big data were highlighted, and modern approaches to processing big data were analyzed. A brief description of the horizontal-scalable architecture and the BI-solution architecture for big data processing is given. The problems of processing large web data are formulated: limiting the speed of access to data, providing access via network protocols through general-purpose networks.An example showing the approach to processing large web data was also implemented. Based on the idea of big data, the described complexities of web data processing and the methods of Data Mining, techniques were proposed for effectively solving the practical problem of processing and searching patterns in a large data array.The following classes have been developed in the C # programming language:Class of receiving web data via the Internet; Data conversion class;Intelligent data processing class;Created DDL script that creates a structure for the accumulation of web data.A single UML class diagram has been developed.The constructed system of data and classes allows to solve the main part of the problems of processing large web data and perform intelligent processing using Data Mining technology in order to solve the problem posed of identifying certain records in a large array. The combination of object-oriented approach, neural networks and BI-analysis to filter data will speed up the process of data processing and obtaining the result of the studyConclusion. According to the results of the study, it can be argued that the current state of technology for analyzing large web data allows you to efficiently process data objects, identify patterns, get hidden data and get full-fledged statistical data.The obtained results can be used both for the purpose of the initial study of big data processing technologies, and as a basis for developing an already real application for analyzing web data. The use of neural networks and the created universal classes-handlers makes the created architecture flexible and self-learning, and the class declarations and the base DDL structure will greatly simplify the development of program code.


2020 ◽  
Vol 3 (506) ◽  
pp. 82-91
Author(s):  
L. O. Chagovets ◽  
◽  
V. V. Chahovets ◽  
A. S. Didenko ◽  
◽  
...  

2012 ◽  
Vol 9 (2) ◽  
pp. 86
Author(s):  
Nurtriana Hidayati

<p>Information is the result of data processing plays an important role in anorganization, especially in decision-making process. Pentaho application of Intelligent Business Products is one of the technologies for collecting, storing, analyzing, and providing access to data to help enterprise users make better business decisions. Pentaho has a function as reporting, analysis, dashboards, data integration (ETL) and data mining. Pentaho is better to manage large and complex data and be able to complete the functional organization.</p>


2013 ◽  
Vol 303-306 ◽  
pp. 1026-1029
Author(s):  
Xue Dong Fan

Abstract. In this paper, a clustering algorithm based on data mining technology applications, the use of the extraction mode noise characteristics amount and pattern recognition algorithms, extraction and selection of the characteristic quantities of the three types of mode, carried out under the same working conditions data mining clustering analysis ultimately satisfying recognition.


Author(s):  
Jaroslav Zendulka

Data mining technology just recently became actually usable in real-world scenarios. At present, the data mining models generated by commercial data mining and statistical applications are often used as components in other systems in such fields as customer relationship management, risk management or processing scientific data. Therefore, it seems to be natural that most data mining products concentrate on data mining technology rather than on the easy-to-use, scalability, or portability. It is evident that employing common standards greatly simplifies the integration, updating, and maintenance of applications and systems containing components provided by other producers (Grossman, Hornick, & Meyer, 2002). Data mining models generated by data mining algorithms are good examples of such components.


Sign in / Sign up

Export Citation Format

Share Document