.png){kind=icon}
返回
Fast approximate minimum spanning tree based clustering algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.07.038.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Minimum Spanning Tree (MST) based clustering algorithms have been employed successfully to detect clusters of heterogeneous nature. Given a dataset of n random points, most of the MST-based clustering algorithms first generate a complete graph G of the dataset and then construct MST from G. The first step of the algorithm is the major bottleneck which takes 0(n(2)) time. This paper proposes an algorithm namely MST-based clustering on partition-based nearest neighbor graph for reducing the computational overhead. By using a centroid based nearest neighbor rule, the proposed algorithm first generates a sparse Local Neighborhood Graph (LNG) and then the approximate MST is constructed from LNG. We prove that both size and computational time to construct the graph (LNG) is 0(n(3/2)), which is a 0(root n) factor improvement over the traditional algorithms. The approximate MST is constructed from LNG in 0(n(3/2) lg n) time, which is asymptotically faster than 0(n(2)). Experimental analysis on both synthetic and real datasets demonstrates that the computational time has been reduced significantly by maintaining the quality of clusters obtained from the approximate MST. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Clustering
Minimum spanning tree
Local neighborhood graph
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏A graph-theoretical clustering method based on two rounds of minimum spanning trees.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏
查看更多内容