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Fusion research and application of the algorithm based on the improvement of the k-means clustering

Author: LiuXiong
Tutor: LiuManLing
School: Central South University
Course: Computer Science and Technology
Keywords: Cluster analysis Clustering Fusion The degree of difference Diff function Weighting function k-means algorithm
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 110
Quote: 0
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Abstract


Clustering fusion method as one of the most important exploratory data mining technology has gradually become a focus for researchers , and a wide range of applications in the telecommunications, banking and finance is a huge amount of information in the field . With the rapid development of communication technology , the major carriers have entered a new era of 3G business run , how to understand the needs of customers in the operation , improve customer service different behavior is the key to the success of major carriers . Clustering algorithm can use data mining techniques to master the user information , to provide high-quality services to the people . This paper analyzes and research at home and abroad in recent years about clustering algorithm and clustering fusion algorithm academic literature , the lack of specific data and applications is only suitable for a single algorithm is proposed based on the improvement of the k-means clustering fusion algorithm . Firstly, define a new cluster members diff function ( Difference Comparison Function DCF ) and the DCF cluster members to judge , choose the smaller average degree of difference as the final integration of the members of the ; then proposed a new weighting function weighted cluster members ; final consensus matrix fusion . Experimental results show that : the new and improved method can effectively deal with the differences of the cluster members , and the accuracy of the clustering results , scalability and robustness perform better than single clustering algorithm . This article will improve clustering fusion algorithm is applied to a communication carrier customer behavior analysis , consumption behavior of customers , the customers of the frequency of use of the product and other research to understand customer behavior preferences to get the carrier customer behavior analysis management information , real data experiments to prove the effectiveness of the algorithm .

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