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Research on an Improved Clustering Algorithm of k_means

Author: LiuZhenGuang
Tutor: LiuJie
School: Harbin Engineering University
Course: Applied Computer Technology
Keywords: Clustering k-means algorithm Grid Average point
CLC: TP311.13
Type: Master's thesis
Year: 2010
Downloads: 157
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Abstract


With the rapid development of networks and information technology continues to progress , the various data expands at an alarming rate , how to find useful information from these data , and data and information for classification, has become increasingly urgent . The emergence of data mining technology , large amounts of data processing possible. Clustering is an important data mining technology, has become one of the hot . This article mainly for k_means algorithms sensitive to outliers and to select sensitive limitations on the initial point , to make two points improvement of k_means algorithm clustering process . First, outlier detection algorithm of k_means depth study of grid-based data preprocessing algorithm . After meshing of data sets , outlier detection . Secondly, the initial point selection analyze k_means algorithm , proposed the initial point selection algorithm based on the average point . The method is based on data pre-processing algorithm based on the grid directly to the initial points in the grid , so that the initial point more reasonable and close to the actual cluster center . Finally, the application of the two algorithms in the the clustering algorithm k_means process , the isolated-point processing and initial point selection is given a improved k_means algorithm . Through experiments the k_means improved verification and analysis , experimental results show that improved k_means algorithm to some extent, improve the accuracy of clustering .

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