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Netcom Customers Based On Data Mining Research And Application Of Market Segmentation

Posted on:2008-01-09Degree:MasterType:Thesis
Country:ChinaCandidate:L WangFull Text:PDF
GTID:2209360212987427Subject:Industrial Economics
Abstract/Summary:PDF Full Text Request
With the development of Data Mining, Data Mining has been used in all aspect of life, especially in the telecom. Data Mining Technologies find interesting knowledge from large datum stored in database or data warehouse. Data Mining Technologies make use of statistics, artificial intelligence, machine learning and database to find hidden patterns, relationship, complete predictive modeling and valuable knowledge.Clustering analysis is an important research direction in data mining technology. The main target of this thesis is how to realize telegraphic customer classification by use of clustering analysis.This paper makes a simple introduce about data mining at first. It mainly probes into the concept of data mining technology, the knowledge classifications of data mining and the data mining tools etc. The most job of this thesis is the application of Data Mining in telegraphic customer classification. In order to resolve this problem, we must analyze telegraphic demand. By the analysis, we could find data which we need. Then we should pick up data and transfer data. In order to realize telegraphic customer classification and gain the logical result, we must choice right clustering algorithm to realize telegraphic customer classification. We use K-means algorithm and Discriminate analysis algorithm to realize subsection. We take use of the outcomes of K-means algorithm as the input of discriminate analysis algorithm, and through compare those outcomes, in the end, we gain right answer.This paper includes six chapters. Chapter one is the introduction of the background, general situation and main study object. Chapter two expands and analyzes the elementary algorisms of data mining. Chapter three is an important chapter. In this chapter, the process of data pretreatment and data mining is discussed and implemented and analyzes with the K-means. Chapter four is the data mining results with discriminate and provides suggestions on the structure and page setup of chapter five. Chapter five structure the system of M plan. Chapter six is the summery and expectation of the whole study.
Keywords/Search Tags:Data Mining, Customer classification, Clustering algorithm, K-means algorithm, Discriminate analysis algorithm
PDF Full Text Request
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