Paper Info
Title
Graph mining: A survey of graph mining techniques
Abstract
Data mining is comprised of many data analysis techniques. Its basic objective is to discover the hidden and useful data pattern from very large set of data. Graph mining, which has gained much attention in the last few decades, is one of the novel approaches for mining the dataset represented by graph structure. Graph mining finds its applications in various problem domains, including: bioinformatics, chemical reactions, Program flow structures, computer networks, social networks etc. Different data mining approaches are used for mining the graph-based data and performing useful analysis on these mined data. In literature various graph mining approaches have been proposed. Each of these approaches is based on either classification; clustering or decision trees data mining techniques. In this study, we present a comprehensive review of various graph mining techniques. These different graph mining techniques have been critically evaluated in this study. This evaluation is based on different parameters. In our future work, we will provide our own classification based graph mining technique which will efficiently and accurately perform mining on the graph structured data.
Year
DOI
Venue
2012
10.1109/ICDIM.2012.6360146
Digital Information Management
Keywords
Field
DocType
data mining,decision trees,pattern classification,pattern clustering,set theory,classification-based graph mining technique,data analysis,data pattern clustering,data set,decision tree data mining techniques,Data Mining,Graph Mining,Sub graphs,frequent graphs
Ontology (information science),Data mining,Decision tree,Data stream mining,Graph database,Concept mining,Data analysis,Computer science,Molecule mining,Artificial intelligence,Cluster analysis,Machine learning
Conference
ISSN
ISBN
Citations 
pending
978-1-4673-2428-1
4
PageRank 
References 
Authors
0.44
9
3
Name
Order
Citations
PageRank
Saif ur Rehman140.44
Asmat Ullah Khan240.44
Simon Fong340.44