2007
DOI: 10.1007/978-0-387-74161-1_2
|View full text |Cite
|
Sign up to set email alerts
|

Clustering Improves the Exploration of Graph Mining Results

Abstract: Abstract. Mining frequent subgraphs is an area of research where we have a given set of graphs, and where we search for (connected) subgraphs contained in many of these graphs. Each graph can be seen as a transaction, or as a molecule -as the techniques applied in this paper are used in (bio)chemical analysis. In this work we will discuss an application that enables the user to further explore the results from a frequent subgraph mining algorithm. Such an algorithm gives the frequent subgraphs, also referred t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2011
2011
2011
2011

Publication Types

Select...
2

Relationship

1
1

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 7 publications
0
1
0
Order By: Relevance
“…In [4] the current setup is used to cluster data; that paper discusses an application that enables the user to further explore the results from a frequent subgraph mining algorithm, by browsing the lattice of frequent graphs. Figure 2: An example of co-occurring subgraphs from [9] with an example molecule.…”
Section: Introductionmentioning
confidence: 99%
“…In [4] the current setup is used to cluster data; that paper discusses an application that enables the user to further explore the results from a frequent subgraph mining algorithm, by browsing the lattice of frequent graphs. Figure 2: An example of co-occurring subgraphs from [9] with an example molecule.…”
Section: Introductionmentioning
confidence: 99%