基于子空间和投影聚类算法的高维数据聚类
Clustering high dimensional data using subspace and projected clustering algorithms
摘要：
问题陈述：聚类有许多在统计学、模式识别、数据采掘和其他领域发展起来的技术。子空间聚类枚举数据集的所有子空间中的对象聚类。它往往会产生许多重叠的集群。方法：子空间聚类和投影聚类是高维空间聚类的研究领域。在本研究中，我们实验了三个面向聚类的算法，PROCLUS，P3 C和STATPC。结果如下：在一般情况下，PROCLUS执行更好的计算时间和产生的最少数量的未聚类的数据，而STATPC优于PROCLUS和P3 C在两个聚类点和相关属性found. Conclusions/Recommendations的准确性：在这项研究中，我们详细分析了不同的数据聚类方法的属性。
英文摘要：
Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. In this research we experiment three clustering oriented algorithms, PROCLUS, P3C and STATPC. Results: In general, PROCLUS performs better in terms of time of calculation and produced the least number of un-clustered data while STATPC outperforms PROCLUS and P3C in the accuracy of both cluster points and relevant attributes found. Conclusions/Recommendations: In this study, we analyze in detail the properties of different data clustering method.
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