Volume 7 Number 1 (Jan. 2012)
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JSW 2012 Vol.7(1): 220-227 ISSN: 1796-217X
doi: 10.4304/jsw.7.1.220-227

Classification and Analysis of Frequent Subgraphs Mining Algorithms

Mohammad Reza Keyvanpour1 and Fereshteh Azizani2

1Department of Computer Engineering, Alzahra University, Tehran, Iran
2Department of Computer Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran

Abstract—In recent years, data mining in graphs or graph mining have attracted much attention due to explosive growth in generating graph databases. The graph database is one type of database that consists of either a single large graph or a number of relatively small graphs. Some applications that produce graph database are as follows: Biological networks, semantic web and behavioral modeling. Among all patterns occurring in graph database, mining frequent subgraphs is of great importance. The frequent subgraph is the one that occurs frequently in the graph database. Frequent subgraphs not only are important themselves but also are applicable in other aspects of data analysis and data mining tasks, such as similarity search in graph database, graph clustering, classification, indexing, etc. So far, numerous algorithms have been proposed for mining frequent subgraphs. This study aims to create overall view of the algorithms through the analysis and comparison of their characterizations. To achieve the aim, the existing algorithms are classified based on their graph database and their subgraph generation way. The proposed classification can be effective in choosing applications appropriate algorithms and determination of graph mining new methods in this regard.

Index Terms—Graph database, Data mining, Graph mining, Frequent subgraph


Cite:Mohammad Reza Keyvanpour and Fereshteh Azizani, "Classification and Analysis of Frequent Subgraphs Mining Algorithms," Journal of Software vol. 7, no.1, pp. 220-227, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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