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Research on Noise and High Dimensional Problems in Clustering

Author: ZhouZuo
Tutor: ZhangWei
School: Jiangnan University
Course: Applied Computer Technology
Keywords: Data Mining Cluster analysis High-dimensional data Map Clustering Association rules Correlation dimension
CLC: TP311.13
Type: Master's thesis
Year: 2006
Downloads: 172
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Abstract


In recent years , as one of the important tools for data mining , clustering techniques have been more and more attention . There are many mature clustering algorithms , and these algorithms are widely used in various related fields . However , most of the clustering algorithm is valid only for low-dimensional data sets , for the increasing number of high-dimensional data sets , the clustering performance is greatly reduced . The proper effective clustering analysis of high-dimensional data sets is a difficult and hot issues in the field of data mining . High -dimensional data clustering analysis of one of the difficulties is the high time complexity , which makes classical clustering algorithms, such as hierarchical clustering , difficult to deal with large-scale high-dimensional data . Another difficulty of high-dimensional data sets is its high level of noise sensitivity , a feature that makes many existing clustering algorithms , such as k-means clustering , hierarchical clustering algorithm performance is greatly reduced . Therefore , the clustering algorithm to give fast and robust for high-dimensional data sets is necessary . Mapped clustering algorithm is to prove that the algorithm compared to classical clustering algorithm is effective for a large class of high-dimensional data sets clustering algorithm , the experimental and theoretical . In this paper, the difficulty of the above-mentioned high-dimensional data clustering , a fast and robust mapping clustering algorithm . The algorithm using the association rules to query each cluster cluster correlation dimension , then the correlation dimension further clustering analysis . The main advantage of the algorithm is that : 1. Rapidity . Better robustness , noise sensitivity and lower 3 . Ability to automatically obtain the number of clusters through several sets of simulation experiments proved above advantages .

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