Paper Info
Title
Automatic extraction of common research areas in world scientograms using the multiobjective Subdue algorithm
Abstract
Scientograms are graph representations of scientific information. Exploring vast amount of scientograms for scientific data analysis has been of great interest in Information Science. This work emphasizes the application of multiobjective subgraph mining for the scientogram analysis task regarding the extraction of common research areas in the world. For this task, we apply a recently proposed multiobjective Subdue (MOSubdue) algorithm for frequent subgraph mining in graph-based data. The algorithm incorporates several ideas from evolutionary multiobjective optimization. The underlying scientogram structure is a social network, i.e., a graph, MOSubdue can uncover common (or frequent) scientific structures to different scientograms. MOSubdue performs scientogram mining by jointly maximizing two objectives, the support (or frequency) and complexity of the mined scientific structures. Experimental results on five realworld datasets from Elsevier-Scopus scientific database clearly demonstrated the potential of multiobjective subgraph mining in scientogram analysis.
Year
DOI
Venue
2012
10.1109/CEC.2012.6256436
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
data mining,evolutionary computation,graph theory,information science,scientific information systems,MOSubdue algorithm,automatic extraction,common research area extraction,common research areas,evolutionary multiobjective optimization,frequent subgraph mining,graph representations,graph-based data,information science,multiobjective Subdue algorithm,multiobjective subdue,multiobjective subgraph mining,scientific data analysis,scientific information,scientogram analysis task,scientogram mining,world scientograms,Evolutionary multiobjective optimization,Frequent subgraph mining,Graph-based data mining,Multiobjective graph-based data mining,NSGA-II,Pareto optimality,Scientograms,Subdue
Data mining,Social network,Computer science,Multi-objective optimization,Artificial intelligence,Graph theory,Approximation algorithm,Graph,Mathematical optimization,Algorithm design,Information science,Algorithm,Evolutionary computation,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4673-1508-1
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
Prakash Shelokar1383.33
Arnaud Quirin216813.68
Oscar Cordón31572100.75