数据流的加权模糊聚类算法

Renxia Wan, Xiaoya Yan, Xiaoke Su
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引用次数: 25

摘要

由于有限的内存可用性和实时查询响应要求,对数据流的挖掘提出了很大的挑战。最重要的挖掘任务之一是聚类。针对数据流的聚类算法已经有很多。模糊聚类是一种重要的聚类方法。然而,据我们所知,所有的聚类算法都是硬聚类方法,模糊聚类算法目前还没有直接用于数据流。模糊c均值(FCM)是一种典型的模糊聚类算法。本文对FCM进行了扩展,提出了一种用于数据流聚类的加权模糊算法。在合成数据集和真实数据集上的实验结果表明,该算法优于传统的FCM算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Weighted Fuzzy Clustering Algorithm for Data Stream
Mining data streams poses great challenges due to the limited memory availability and real time query response requirement. One of the most important mining tasks is clustering. There already lots of clustering algorithms for data stream have been presented. Fuzzy cluster is an important clustering method. However, to the best of our knowledge, all the clustering algorithms are hard clustering methods, fuzzy clustering algorithm is presently not used directly for data streams. Fuzzy c-means (FCM) is a typical fuzzy clustering algorithm. In this paper, we extend FCM and propose a weighted fuzzy algorithm for clustering data stream. Experimental results on both synthetic and real data sets show its superiority over the traditional FCM algorithms.
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