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Research on Application of Data Mining Technology in Degree of Satisfaction Analysis of Television Customers

Author: ZuoHuaLi
Tutor: ZhengCheng
School: Anhui University
Course: Applied Computer Technology
Keywords: Data Mining Classification Clustering Customer Satisfaction
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 53
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Data mining is the raw data from a large number of messages, dig out meaningful data to the user and to guide people's practice, resulting in efficiency and profit. In this paper, a brief overview of the second chapter of theoretical knowledge of data mining, including data mining concepts and methods. In the third chapter of the classification presentation, we classify the concept, characteristics and data processing steps are carried out briefly enumerate and description. In the paper also describes two main clustering and classification of their analytical methods, in turn gave the analytical method algorithm steps and processes. In this article, the author cites the classic K-means clustering algorithm, which cluster centers usually mean the class to find the most appropriate cluster center. In this article, the author uses data mining techniques in cluster analysis and classification methods of data reduction factor analysis method, the same two methods of data analysis, so easy in comparison and analysis. To make this theory in practice has a good application, in Chapters IV and V of this article, the author of the collected data through the fill empty, clean finishing, removing noise and other rules of procedure after the data, using statistical software SPSS for Windows 13.0 The data were carefully analyzed, and draw the corresponding experimental results, the author uses a direct form of experimental results and analysis of data, in addition to tables and graphs gravel diagrams for a direct display, the experimental data and results at a glance. After conducting a complete course of the experiment after experiment based on the results obtained, the data were further analyzed and give constructive comments and suggestions, analysis of customer satisfaction in the product are not satisfied with the reasons Due to limited space and enhance the characteristics of two analytical algorithms compare product satisfaction only in this aspect of the experiment, the experimental results are compared, and then draw conclusions. This addition to the basic theoretical knowledge of data mining brief, but also on data mining techniques in clustering and classification techniques were focused on introduction and description. An extensive branch network in a rural part of user satisfaction feedback form the basis of the data collected, the data in the product satisfaction and service satisfaction satisfaction in the two sub-items clustering and classification Factor analysis of descriptive analysis, the relevant experimental results obtained after the implementation of the entire process of knowledge discovery, come to the place where the customer is not satisfied, the same token, we can on this basis, for more data for analysis , draw more data we want to know, for the company to improve digital television surrounding some product quality and service quality to provide a meaningful basis for decision making relative.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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