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Based on cluster analysis of data mining algorithms

Author: SunXiaoPing
Tutor: CaoXieDong
School: Southwest Petroleum Institute
Course: Computer Applications
Keywords: Data Mining and Knowledge Discovery Knowledge discovery in databases Cluster analysis Dissimilarity Noise data Outlier
CLC: TP311.12
Type: Master's thesis
Year: 2002
Downloads: 634
Quote: 23
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Abstract


Data mining is a hot research topic in many areas , attracted the attention of a large number of researchers . Cluster analysis is an important data mining functions , it can be said that a research branch of data mining . Today , research in this area is focused on a clustering algorithm to produce a good performance but also practical clustering algorithm is its ultimate goal . A general introduction as the basis of this article first data mining , is designed to form an overall understanding of the data mining . Detailed study on this basis, the various existing main representative of the clustering algorithm , the completion of the analysis of the specific circumstances of their disadvantages and advantages , as well as the respective adaptation and an overall multifaceted performance on various algorithms . relatively comprehensive description and compare ; further division method class classical algorithm need to give the result of the number of clusters K largest insufficient discussion of some of its improved method . The most important thing is that leads to a new clustering algorithm : based on the distribution of the large-scale the database clustering learning ( DBCLAD ) , it not only avoids the need given by the user is difficult to determine the parameters of the embarrassment of any shape and can be found cluster , is a very advantage promising algorithm . Finally, detailed concrete realization of the algorithm and apply it to an instance .

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