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Context Selection on Attributed Graphs for Outlier and Community Detection

Iglesias Sánchez, Patricia

Abstract:
Today's applications store large amounts of complex data that combine information of different types. Attributed graphs are an example for such a complex database where each object is characterized by its relationships to other objects and its individual properties. Specifically, each node in an attributed graph may be characterized by a large number of attributes. In this thesis, we present different approaches for mining such high dimensional attributed graphs.

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Volltext §
DOI: 10.5445/IR/1000048502
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Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Hochschulschrift
Jahr 2015
Sprache Englisch
Identifikator urn:nbn:de:swb:90-485022
KITopen-ID: 1000048502
Verlag KIT, Karlsruhe
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Programmstrukturen und Datenorganisation (IPD)
Prüfungsdaten 11.05.2015
Referent/Betreuer Prof. K. Böhm
Schlagworte data mining, attributed graphs, high dimensional, homophily, outliers, community detection
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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