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Ant colony algorithm and its application on Web Mining Clustering

Author: GongZuo
Tutor: JiangHong
School: East China Normal University
Course: Applied Computer Technology
Keywords: Web Mining Clustering Ant Colony Algorithm Ant Colony Clustering Algorithm LF algorithm
CLC: TP311.13
Type: Master's thesis
Year: 2010
Downloads: 199
Quote: 1
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Abstract


Today's human society is the information society. In science and technology , especially network technology , driven by the World Wide Web both in depth and breadth are developing rapidly. Internet has changed our lives in unprecedented , while bringing along with geometric data . In these massive , heterogeneous information resources , has great potential value contains the knowledge of how to extract from these data sources a credible , effective , useful information for people in today's society to become a major research focus, data mining technology is in this context to generate and develop. This paper studies the ant colony clustering algorithm to Web Clustering mining analysis applications, through the existing ant colony clustering algorithm, the algorithm has better performance , and thus more suitable for Web mining clustering analysis, the main contents include : first overview of data mining and Web mining , data mining and Web mining for some basic concepts , such as data mining system some basic knowledge of data mining are introduced , and then the data mining clustering analysis techniques are described introduce the basic concepts of clustering , clustering technology research status quo and comparative analysis of the current of the more important categories of clustering methods . Secondly introduce the basic model of ant colony clustering algorithm based on the basic model and based on ant colony clustering algorithm LF , LF -depth analysis of the advantages and disadvantages of the algorithm to improve on its basis , an improved ant colony clustering algorithm, simulation results show that the improved algorithm than the LF algorithm has better performance , solve the LF algorithm converges relatively slow , iterative cycle length shortcomings and makes the improved clustering algorithm is more suitable for Web mining clustering analysis . Finally, improved clustering : the algorithm based on the establishment of a small Web mining clustering system .

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