将模糊c均值扩展到数据流聚类

20th Iranian Conference on Electrical Engineering (ICEE2012) Pub Date : 2012-05-15 DOI:10.1109/IRANIANCEE.2012.6292449

S. Mostafavi, A. Amiri

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引用次数: 7

摘要

数据流是一个有序和连续的示例序列，只能检查一次。与传统的挖掘算法相比，数据流挖掘带来了新的挑战。模糊c均值(FCM)是一种聚类方法，其中一个数据点可以同时分配给多个聚类。本文将FCM算法扩展到数据流聚类。在KDD-CUP'99数据集上的性能实验表明了该算法的有效性。

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Extending fuzzy c-means to clustering data streams

A data stream is an ordered and continuous sequence of examples that can be examined only once. Data stream mining introduces new challenges compared to traditional mining algorithms. Fuzzy c-means (FCM) is a method of clustering in which a data point can assign to more than one cluster at the same time. In this paper we extend FCM algorithm to clustering data streams. Our performance experiments over KDD-CUP'99 data set show the efficiency of the algorithm.

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20th Iranian Conference on Electrical Engineering (ICEE2012)

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