HSC: A spectral clustering algorithm combined with hierarchical method

Title:

HSC: A spectral clustering algorithm combined with hierarchical method

Creator:

Liu, Li, Chen, Xiwei, Luo, Dashi, Lu, Yonggang, Xu, Guandong, and Liu, Ming

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:f400cd1b-5304-41e6-9d46-529f71acdaeb
uuid:f400cd1b-5304-41e6-9d46-529f71acdaeb
doi:10.14311/NNW.2013.23.031

Subject:

Data mining, clustering, spectral clustering, and hierarchical clustering

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2013 Volume:23 | Number:6

Harvested from:

CDK

Metadata only:

false