维间模糊聚类

ACM SE '10 Pub Date : 2010-04-15 DOI:10.1145/1900008.1900127
Yong Shi
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引用次数: 1

摘要

本文介绍了利用模糊概念对多维数据进行聚类检测的研究。聚类分析是数据挖掘的一个重要分支。已经设计了许多算法来检测聚类。然而,很难分析不同维度之间的相互关系。在本文中,我们提出了一种利用模糊概念来分析和量化相关维度之间相互关系的新方法。模糊概念是一种概念,其内容、价值或应用的边界可以根据上下文或条件而变化,而不是一劳永逸地固定下来。我们应用模糊概念来帮助改进聚类过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inter-dimensional fuzzy clustering
In this paper, we present our research on detecting clusters for multi-dimensional data using fuzzy concepts. Cluster analysis is an important sub-field in data mining. Many algorithms have been designed to detect clusters. However, it is difficult to analyze the inter-relationship among different dimensions. In this paper, we propose a novel approach to analyze and quantify the inter-relationship among correlated dimensions using the Fuzzy concept. A fuzzy concept is a concept of which the content, value, or boundaries of application can vary according to context or conditions, instead of being fixed once and for all. We apply the Fuzzy concept to help improve the clustering process.
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