VACULÍK, Karel. A Versatile Algorithm for Predictive Graph Rule Mining. Online. In Jakub Yaghob. Proceedings ITAT 2015: Information Technologies - Applications and Theory. 1. vydání. Praha: CEUR-WS.org, 2015, p. 51-58. ISBN 978-1-5151-2065-0.
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Basic information
Original name A Versatile Algorithm for Predictive Graph Rule Mining
Authors VACULÍK, Karel (203 Czech Republic, guarantor, belonging to the institution).
Edition 1. vydání. Praha, Proceedings ITAT 2015: Information Technologies - Applications and Theory, p. 51-58, 8 pp. 2015.
Publisher CEUR-WS.org
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/15:00084898
Organization unit Faculty of Informatics
ISBN 978-1-5151-2065-0
ISSN 1613-0073
Keywords (in Czech) dolování z grafů; dolování z dat; dynamické grafy; dolování pravidel; časté vzory; predikce
Keywords in English graph mining; data mining; dynamic graphs; rule mining; frequent patterns; prediction
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 28/4/2016 15:30.
Abstract
Pattern mining in dynamic graphs has received a lot of attention in recent years. However, proposed methods are typically limited to specific classes of patterns expressing only a specific types of changes. In this paper, we propose a new algorithm, DGRMiner, which is able to mine patterns in the form of graph rules capturing various types of changes, i.e. addition and deletion of vertices and edges, and relabeling of vertices and edges. This algorithm works both with directed and undirected dynamic graphs with multiedges. It is designed both for the single-dynamic-graph and the set-of-dynamic-graphs scenarios. The performance of the algorithm has been evaluated by using two real-world and two synthetic datasets.
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