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Context Selection on Attributed Graphs for Outlier and Community Detection

Iglesias Sánchez, Patricia

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
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Hochschulschrift
Publikationsjahr 2015
Sprache Englisch
Identifikator urn:nbn:de:swb:90-485022
KITopen-ID: 1000048502
Verlag Karlsruher Institut für Technologie (KIT)
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Programmstrukturen und Datenorganisation (IPD)
Prüfungsdaten 11.05.2015
Referent/Betreuer Prof. K. Böhm
Schlagwörter data mining, attributed graphs, high dimensional, homophily, outliers, community detection
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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