Efficient algorithm for projected clustering

Proceedings 18th International Conference on Data Engineering Pub Date : 2002-02-26 DOI:10.1109/ICDE.2002.994727

Eric Ka Ka Ng, A. Fu

引用次数: 7

Abstract

With high-dimensional data, natural clusters are expected to exist in different subspaces. We propose the EPC (efficient projected clustering) algorithm to discover the sets of correlated dimensions and the location of the clusters. This algorithm is quite different from previous approaches and has the following advantages: (1) there is no requirement on the input regarding the number of natural clusters and the average cardinality of the subspaces; (2) it can handle clusters of irregular shapes; (3) it produces better clustering results compared to the best previous method; (4) it has high scalability. From experiments, it is several times faster than the previous method, while producing more accurate results.

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高效的投影聚类算法

对于高维数据，期望自然聚类存在于不同的子空间中。我们提出了EPC(高效投影聚类)算法来发现相关维集和聚类的位置。该算法与以往的方法有很大的不同，具有以下优点:(1)对输入的自然聚类个数和子空间的平均基数没有要求;(2)能够处理不规则形状的簇;(3)与之前最优的聚类方法相比，聚类效果更好;(4)具有较高的可扩展性。实验结果表明，该方法的速度比以前的方法快几倍，同时得到的结果更准确。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings 18th International Conference on Data Engineering

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