基于散/聚模型提高在线聚类算法精度的新方法

Kian Farsandaj, Chen Ding, A. Sadeghian
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引用次数: 0

摘要

聚类分析用于数据挖掘,能够从数据集中提取有趣的数据模式,在聚类分析过程中,准确性和效率是起关键作用的因素。Scatter/Gather是一种基于聚类的浏览模型,以往关于该模型的研究大多集中在聚类算法的效率上。本文提出了一种既能提高在线聚类算法的精度,又能保持合理的效率水平的算法。实验证明,新算法比原算法精度更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new approach to improve the accuracy of online clustering algorithm based on scatter/gather model
In cluster analysis process used in data mining which enables extracting interesting data patterns from datasets, accuracy and efficiency are the factors which play a pivotal role. Scatter/Gather is a cluster-based browsing model, and most of previous works on this model focused on efficiency of the clustering algorithm. In this paper we present an algorithm which could improve the accuracy of the online clustering algorithm while still maintain a reasonable level of efficiency. Our experiment proves that the new algorithm is more accurate than the original algorithm.
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