Title :
Mining of Frequent Externally Extensible Outerplanar Graph Patterns
Author :
Yamasaki, Hitoshi ; Shoudai, Takayoshi
Author_Institution :
Dept. of Inf., Kyushu Univ., Fukuoka
Abstract :
An outerplanar graph is a planar graph which can be embedded in the plane in such a way that all of vertices lie on the outer boundary. Many chemical compounds are known to be expressed by outerplanar graphs. In this paper, firstly, we introduce an externally extensible outerplanar graph pattern (eeo-graph pattern for short) as a graph pattern common to a finite set of outerplanar graphs like a dataset of chemical compounds. The eeo-graph pattern can express a substructure common to blocks which appear in outerplanar graph structured data. Secondly, we propose a data mining algorithm for enumerating all maximal frequent eeo-graph patterns from a finite set of outerplanar graphs. Finally, we report experimental results on a chemical dataset.
Keywords :
data mining; graph theory; spatial data structures; chemical compound; data mining algorithm; maximal frequent externally-extensible outerplanar graph pattern mining; outerplanar graph structured data; Bridges; Chemical compounds; Data mining; Databases; Feature extraction; Informatics; Machine learning; Polynomials; Tree graphs; Web sites; chemical dataset; graph mining; graph structured pattern; outerplanar graph; pattern discovery;
Conference_Titel :
Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-0-7695-3495-4
DOI :
10.1109/ICMLA.2008.98