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Graph Mining: A Survey of Graph Mining Techniques
Saif Ur Rehman1; Asmat Ullah Khan2; Simon Fong3
Conference NameSeventh International Conference on Digital Information Management (ICDIM 2012)
Source Publication7th International Conference on Digital Information Management, ICDIM 2012
Conference Date22-24 Aug. 2012
Conference PlaceMacau, China

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. © 2012 IEEE.

KeywordData Mining Frequent Graphs Graph Mining Sub Graphs
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Document TypeConference paper
Affiliation1.CORD: Center for Research in Data Engineering Mohammad Ali Jinnah University Islamabad, Pakistan
2.Department of Computer Science Shaheed Zulifiqar Ali Bhutto Institute of Science and Technology (SZABIST) Islamabad, Pakistan
3.Department of Computer and Information Science University of Macau Taipa, Macau SAR
Recommended Citation
GB/T 7714
Saif Ur Rehman,Asmat Ullah Khan,Simon Fong. Graph Mining: A Survey of Graph Mining Techniques[C]:IEEE,2012:88-92.
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