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A K-MEANS Clustering Algorithm Based on ALPHA-STABLE Distribution

Author: XuMingZhe
Tutor: ZhangJunYing
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: K_MEANS Clustering algorithm ALPHA-STABLE distribution Gaussian distribution Fractional lower order moments p - norm
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 56
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ALPHA-STABLE distribution of a large number exist in real life , it is currently one of the international study comparing popular topics ALPHA-STABLE distribution a good description of the distribution of real-world data , it has gradually been applied to a variety of fields , nature more and more people understand . This article is mainly for obedience ALPHA-STABLE distribution data clustering analysis . In recent years , the study of the clustering algorithm has achieved considerable development , including K-MEANS algorithm has been widely used in its algorithm is simple , efficient performance . This paper studies found that K-MEANS algorithm suitable for clustering obey variance Gaussian distribution of data , statistical significance , it is mainly the use of second-order statistics of the data . But to obey the ALPHA-STABLE distribution data , the ALPHA bands ( ALPHA lt; 2 ) as well as higher-order statistics is infinite , the paper argues that the K-MEANS algorithm is not suitable to do clustering obey ALPHA-STABLE distribution data directly analysis . Based on the above analysis , this paper presents a K-MEANS clustering algorithm based ALPHA-STABLE distribution . The algorithm is based on the following ideas : thought K-MEANS clustering algorithm based on fractional lower order moments ideological similarity measure that p - norm ; measure p - norm Gaussian data distribution , which can take advantage of the idea of K-MEANS algorithm indirect clustering data , this would resolve the K-MEANS clustering algorithm for problem obey ALPHA-STABLE distribution data clustering . Experimental results show that it is applied to the simulation data and real data , clustering algorithm proposed in this paper to improve the clustering performance .

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