NDLI: Robust Clustering with Topological Graph Partition

Content Provider	IET Digital Library
Author	Wang, Shuliang Li, Qi Yuan, Hanning Geng, Jing Dai, Tianru Deng, Chenwei
Abstract	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 subgraph 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.
Starting Page	76
Ending Page	84
Page Count	9
ISSN	10224653
Volume Number	28
e-ISSN	20755597
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/28/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2018.09.005
Journal	Chinese Journal of Electronics
Publisher Date	2019-01-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Big Data Combinatorial Mathematics Computer Vision And Image Processing Technique Cut Threshold Data Handling Technique Data Sets Decision Graph Graph Theory Pattern Clustering Probability Robust Clustering Statistics Sub-Graph Topological Graph Partition
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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