Spectral Cluster Maps Versus Spectral Clustering

Autor:	Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
Rok vydání:	2020
Předmět:	ComputingMethodologies_PATTERNRECOGNITION Kernel method Computer science Euclidean space Graph embedding Embedding Graph (abstract data type) Cluster analysis Algorithm Spectral clustering Clustering coefficient
Zdroj:	Computer Information Systems and Industrial Management ISBN: 9783030476786 CISIM
Popis:	The paper investigates several notions of graph Laplacians and graph kernels from the perspective of understanding the graph clustering via the graph embedding into an Euclidean space. We propose hereby a unified view of spectral graph clustering and kernel clustering methods. The various embedding techniques are evaluated from the point of view of clustering stability (with respect to k-means that is the algorithm underpinning the spectral and kernel methods). It is shown that the choice of a fixed number of dimensions may result in clustering instability due to eigenvalue ties. Furthermore, it is shown that kernel methods are less sensitive to the number of used dimensions due to downgrading the impact of less discriminative dimensions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2b23f7745ad3297ef01437caa3f0e520 https://doi.org/10.1007/978-3-030-47679-3_40 Zobrazit plný text záznamu