Detailed Information on Publication Record
2016
Graph Mining: Applications (invited talk)
VACULÍK, KarelBasic information
Original name
Graph Mining: Applications (invited talk)
Name (in English)
Graph Mining: Applications (invited talk)
Authors
VACULÍK, Karel (203 Czech Republic, guarantor, belonging to the institution)
Edition
Bratislava, Proceedings in Informatics and Information Technologies. Bratislava: WIKT & DaZ, p. 31-34, 4 pp. 2016
Publisher
Nakladatel’stvo STU
Other information
Language
Czech
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Slovakia
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
RIV identification code
RIV/00216224:14330/16:00091614
Organization unit
Faculty of Informatics
ISBN
978-80-227-4619-9
Keywords in English
graph mining; network analysis; data mining; classification; anomaly detection; community detection; recommendation
Tags
International impact
Změněno: 14/11/2016 09:05, RNDr. Karel Vaculík, Ph.D.
V originále
Traditional data mining algorithms typically assume data instances to be independent. However, there is a lot of real-world scenarios where relationships between data instances exist and they are principal for data understanding. For example, there are relationships between people in social networks, between chemical elements in chemical compounds, etc. It is difficult or even impossible to express such information in the classical attribute-value representation. Graph mining is an area of data mining that uses a graph representation of data and it allows us to exploit the relationships in the data. The goal of this talk is to present diverse successful applications of graph mining on real-world graphs.
In English
Traditional data mining algorithms typically assume data instances to be independent. However, there is a lot of real-world scenarios where relationships between data instances exist and they are principal for data understanding. For example, there are relationships between people in social networks, between chemical elements in chemical compounds, etc. It is difficult or even impossible to express such information in the classical attribute-value representation. Graph mining is an area of data mining that uses a graph representation of data and it allows us to exploit the relationships in the data. The goal of this talk is to present diverse successful applications of graph mining on real-world graphs.
Links
| MUNI/A/0945/2015, interní kód MU |
|