Robust Clustering with Topological Graph Partition. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Wang, Shuliang; Li, Qi; Yuan, Hanning; Geng, Jing; Dai, Tianru; Deng, Chenwei
• Is Part Of: Chinese journal of electronics. Volume 28:Issue 1(2019)
• Page Start: 76
• Page End: 84
• Publication Date: 2019-01-01
• Subjects: Clustering -- Topological graph partition (TGP) -- Decision graph under probability -- Noise edge; graph theory -- pattern clustering -- probability; robust clustering -- Topological graph partition -- big data -- decision graph -- cut threshold -- sub‐graphs -- data sets; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2018.09.005
• Languages: English
• ISSNs: 1022-4653

Abstract

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.