An Improved Spectral Clustering Algorithm Using Geodesic Distance

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An Improved Spectral Clustering Algorithm Using Geodesic Distance

更新时间：2025-05-08

- An Improved Spectral Clustering Algorithm Using Geodesic Distance
- Vol. 46, Issue 8, Pages: 1-7(2012)
- 作者机构：
  
  1. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
  2. 西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Online First：10 August 2012，
  
  Published：2012
- 稿件说明：
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An Improved Spectral Clustering Algorithm Using Geodesic Distance[J]. 2012, 46(8): 1-7.
DOI：

An Improved Spectral Clustering Algorithm Using Geodesic Distance[J]. 2012, 46(8): 1-7. DOI：

摘要

针对往复式压缩机故障数据空间分布复杂、常规算法不能有效聚类的问题

提出了一种改进的谱聚类算法.该算法使用新的相似度矩阵计算方式

根据故障数据流形分布的特点引入测地线距离取代欧氏距离作为数据间的关系度量; 通过计算各数据点的邻域密度因子有效地识别和剔除了噪声点; 利用基于密度的局部欧氏距离调整方法对流形间隙过小的区域进行了处理.在几个人工数据集和往复式压缩机故障数据集上的测试结果表明

改进谱聚类算法对于具有流形分布、多尺度、有噪声、流形间隙过小甚至交叉等特点的数据具有很好的聚类能力

聚类准确率比常规的k-均值和MSCA谱聚类算法分别提高了50.86%和8.6%.

Abstract

An improved spectral clustering algorithm is proposed to focus on the problem that the general clustering algorithms are invalid for reciprocating compressor fault data lying on complex manifold. A new affinity matrix is obtained. The geodesic distance replaces the traditional Euclidian distance to measure the similarity of data

and neighborhood-based density factor is used to identify and to remove noise points. Moreover

density-based local Euclidian distance adjustment is introduced into areas with small gap between manifolds. The proposed method is implemented on several artificial datasets and a real reciprocating compressor fault dataset. Experimental results show that the new algorithm can accomplish the clustering for data with noise and multi-scale character

especially when the manifolds have small gaps or crossover between each other. Its accuracy is 50.86% and 8.6% higher than those of k-means and MSCA respectively.

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references

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柳炳祥 2

焦李成 1

贾建华 1

闫小喜 1

韩崇昭 1

Related Institution

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University

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