Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors

Autor:	Brigitte Boden, Stephan Günnemann, Ines Färber, Thomas Seidl
Rok vydání:	2013
Předmět:	Knowledge extraction Computer science Feature vector Subgraph isomorphism problem Induced subgraph isomorphism problem Data mining Executable computer.file_format Cluster analysis computer.software_genre computer Graph Subspace topology
Zdroj:	Advances in Knowledge Discovery and Data Mining ISBN: 9783642374524 PAKDD (1)
DOI:	10.1007/978-3-642-37453-1_22
Popis:	Large graphs are ubiquitous in today’s applications. Besides the mere graph structure, data sources usually provide information about single objects by feature vectors. To realize the full potential for knowledge extraction, recent approaches consider both information types simultaneously. Thus, for the task of clustering, combined clustering models determine object groups within one network that are densely connected and show similar characteristics. However, due to the inherent complexity of such a combination, the existing methods are not efficiently executable and are hardly applicable to large graphs.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2ef17ab6560f77fe43a7cdd06c049660 https://doi.org/10.1007/978-3-642-37453-1_22 Zobrazit plný text záznamu