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Improved K-means Clustering Based on Genetic Algorithm

Author: WangMin
Tutor: YinSiQing
School: University of North
Course: Applied Computer Technology
Keywords: Clustering algorithm K-means algorithm Initial cluster centers Outlier Genetic Algorithms
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 131
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Abstract


Greatly improve with the development of science and technology , data acquisition and storage technologies in various fields have accumulated a large amount of data , but now analyzing the data , the ability to acquire knowledge and the laws of the far reach of the growing potential information in the data requirements this subject came into this data mining . People have the ability to recognize the true value of data potentially data mining , and it is one of the databases and information areas of decision-making of the most cutting-edge research direction . Cluster analysis is an important research direction of data mining , clustering people can identify global distribution mode , as well as the potential relationship between data attributes . K-means algorithm is a simple clustering algorithm partitioning algorithm , it has a lot of features , the algorithm is simple , fast convergence , and can effectively handle large data sets . K-means algorithm , however , there is a lot less than the value of K can not determine the clustering result is sensitive to the initial cluster centers affected by the outliers affect large . This article describes a clustering algorithm , the K-means algorithm and improved genetic algorithm is introduced for its shortcomings . Genetic algorithm detailed description and analysis of various genetic manipulation and genetic parameters of the genetic algorithm design improvements based on genetic algorithm K-means clustering algorithm , a good solution to the sensitive issue of the initial cluster centers , improve the algorithm 's global search capability , and reduce the impact of isolated points . First , the use of genetic algorithm global search for initial cluster centers to find the optimal initial cluster centers , and run improved K-means algorithm , the local search capabilities through the K-means algorithm eventually find the best cluster centers . Secondly, the profile cluster centers in clustering iterative process does not take cluster center as a next generation of the mean value of all of the objects in the class , but with a small part of the subset of the center distance of the mean as the next generation of cluster center to to address the impact of outlier . Finally, the use of the existing standard data the experiment of the proposed algorithm , and the experimental results are compared with traditional K-means algorithm and improved algorithm results prove the effectiveness of the proposed algorithm .

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