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Robust Clustering with Topological Graph Partition?

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Clustering is fundamental in many fields with big data. In this paper, a novel method based on Topological graph partition (TGP) is proposed to group objects. A topological graph is created for a data set with many objects, in which an object is connected to k nearest neighbors. By computing the weight of each object, a decision graph under probability comes into being. A cut threshold is conveniently selected where the probability of weight anomalously becomes large. With the threshold, the topological graph is cut apart into several sub-graphs after the noise edges are cut off, in which a connected sub-graph is treated as a cluster. The compared experiments demonstrate that the proposed method is more robust to cluster the data sets with high dimensions, complex distribution, and hidden noises. It is not sensitive to input parameter, we need not more priori knowledge.

ClusteringTopological graph partition (TGP)Decision graph under probabilityNoise edge

WANG Shuliang、LI Qi、YUAN Hanning、GENG Jing、DAI Tianru、DENG Chenwei

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School of software, Beijing Institute of Technology, Beijing 100081, China

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

This work is supported by National Key Research and Development Plan of ChinaThis work is supported by National Key Research and Development Plan of ChinaNational Natural Science Fund of ChinaFrontier and interdisciplinary innovation program of Beijing Institute of Technology

2016YFC08030002016YFB0502604614720392016CX11006

2019

10.1049/cje.2018.09.005

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(1)
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