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Clustering fusion algorithm and its application in the application of mobile communication enterprises

Author: LuoHao
Tutor: LiaoZhiFang
School: Central South University
Course: Applied Computer Technology
Keywords: Data Mining Clustering Fusion A measure of the difference Shared nearest neighbor Customer Segmentation
CLC: F626
Type: Master's thesis
Year: 2009
Downloads: 90
Quote: 4
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Abstract


Clustering, one of the hot spots as data mining technology research , by more and more attention . The main task of clustering data set is divided into a group of meaningful or useful . With the rapid development of database technology , information and data in all walks of life also increased sharply , and the type of data from a single numeric , text gradually transformed into hybrid , which cluster analysis techniques proposed new requirements. Effective algorithm for handling mixed data from the existing literature is relatively small . Based on this situation, this paper focuses on the clustering of mixed data fusion algorithm , discussed its application in the mobile communications industry, at the same time . The study compared the existing algorithms , a fusion of graph-based clustering algorithm - GCE algorithm . The algorithm selects able to handle k-prototypes algorithm and CBL algorithm for mixed data as a cluster membership fusion Pictured basis , using the weights of the vertices and edges in FIG settings to determine the link between the data point by data point shared between the number of nearest neighbors to determine the fusion function . The results show that the algorithm is not only good to deal with mixed attribute data , and more superior results than a single algorithm through experiments on public datasets . In addition, the analysis of the relationship between the four kinds of cluster membership diversity measure and integration accuracy , the experimental results show that the members of the size of about 15-20 data sets to be clustering uniform cluster distribution , diversity measure with the highest degree of correlation between the performance of fusion . Finally this clustering fusion algorithm is successfully applied to the mobile communications business customer segments . Attributes of user information , call behavior , service behavior data mining , the analysis of the relationship between the call behavioral characteristics of the user base and the type of service characteristics, and the user base and revenue , the experimental results confirm that the clustering algorithm effectiveness.

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