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Sebastian Böttger

dblp:11/4871 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › structured data mining › graph mining › graph learning
graph kernels
0.112006
VGM: visual graph mining · SIGMOD Conference 2006
Data mining › structured data mining
graph mining
0.112006
VGM: visual graph mining · SIGMOD Conference 2006
Data mining › predictive modeling
classification
0.012006
VGM: visual graph mining · SIGMOD Conference 2006

Methods — techniques the papers use, named apart from their topics

support vector machine · 0.1
YearPublicationVenuePosition
2006 VGM: visual graph mining
abstract
As more and more graph data become available in various application domains, graph mining is of ever increasing importance in data management.Graph kernels are a novel and successful method for data mining in graphs. Unfortunately, implementing graph kernels is not trivial, and few applied researchers have therefore used graph kernels so far. In this demonstration, we present a Java software package called Visual Graph Mining (VGM). VGM allows the user to classify graphs using graph kernels and Support Vector Machines in a graphical user interface that is easy to learn and use. It is linked to LIBSVM for Support Vector Machine computations, yet can be easily transferred to other Support Vector Machine packages. Furthermore, VGM provides basic data mining features such as Nearest Neighbor search, graph algorithms such as Dijkstra, Floyd-Warshall, and computes and visualizes product graphs and topological indices of graphs.VGM 's homepage can be found at: http://www.cip.ifi.lmu.de/~boettger/sigmod.
Karsten M. Borgwardt, Sebastian Böttger, Hans-Peter Kriegel
SIGMOD Conference2