A new proposal for graph classification using frequent geometric subgraphs

Geometric graph mining has been identified as a need in many applications. This technique detects recurrent patterns in data taking into account some geometric distortions. To meet this need, some graph miners have been developed for detecting frequent geometric subgraphs. However, there are few works that attend to actually apply this kind of pattern as feature for classification tasks. In this paper, a new geometric graph miner and a framework, for using frequent geometric subgraphs in classification, are proposed. Our solution was tested in the already reported AIDS database. The experimentation shows that our proposal gets better results than graph-based classification using non-geometric graph miners.

[1]  José Eladio Medina-Pagola,et al.  ACONS: A New Algorithm for Clustering Documents , 2007, CIARP.

[2]  Frans Coenen,et al.  A survey of frequent subgraph mining algorithms , 2012, The Knowledge Engineering Review.

[3]  George Karypis,et al.  Frequent Substructure-Based Approaches for Classifying Chemical Compounds , 2005, IEEE Trans. Knowl. Data Eng..

[4]  Hiroki Arimura,et al.  Time and Space Efficient Discovery of Maximal Geometric Graphs , 2007, Discovery Science.

[5]  Tsau Young Lin,et al.  Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November - 2 December 2001, San Jose, California, USA , 2001 .

[6]  Robert P. W. Duin,et al.  Prototype selection for dissimilarity-based classifiers , 2006, Pattern Recognit..

[7]  Sebastian Nowozin,et al.  Weighted Substructure Mining for Image Analysis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[9]  YenLuh,et al.  Graph nodes clustering with the sigmoid commute-time kernel , 2009 .

[10]  Kaspar Riesen,et al.  Graph Embedding in Vector Spaces by Means of Prototype Selection , 2007, GbRPR.

[11]  Robert P. W. Duin,et al.  The Dissimilarity Representation for Pattern Recognition - Foundations and Applications , 2005, Series in Machine Perception and Artificial Intelligence.

[12]  Takashi Washio,et al.  An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data , 2000, PKDD.

[13]  Frans Coenen,et al.  Corpus callosum MR image classification , 2010, Knowl. Based Syst..

[14]  Wei Wang,et al.  Efficient mining of frequent subgraphs in the presence of isomorphism , 2003, Third IEEE International Conference on Data Mining.

[15]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[16]  Jintao Zhang,et al.  An efficient graph-mining method for complicated and noisy data with real-world applications , 2011, Knowledge and Information Systems.

[17]  Sebastian Nowozin,et al.  Frequent Subgraph Retrieval in Geometric Graph Databases , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[18]  Yannis Manolopoulos,et al.  Mining patterns from graph traversals , 2001, Data Knowl. Eng..

[19]  José Eladio Medina-Pagola,et al.  Frequent approximate subgraphs as features for graph-based image classification , 2012, Knowl. Based Syst..

[20]  José Francisco Martínez Trinidad,et al.  Mining Frequent Connected Subgraphs Reducing the Number of Candidates , 2008, ECML/PKDD.

[21]  Keun Ho Ryu,et al.  Approximate weighted frequent pattern mining with/without noisy environments , 2011, Knowl. Based Syst..

[22]  Lawrence B. Holder,et al.  Fuzzy Substructure Discovery , 1992, ML.

[23]  George Karypis,et al.  Discovering frequent geometric subgraphs , 2007, Inf. Syst..

[24]  Christian Borgelt,et al.  Mining molecular fragments: finding relevant substructures of molecules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[25]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[26]  George Karypis,et al.  Frequent subgraph discovery , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[27]  Horst Bunke,et al.  Transforming Strings to Vector Spaces Using Prototype Selection , 2006, SSPR/SPR.

[28]  Selim Aksoy,et al.  Image Classification Using Subgraph Histogram Representation , 2010, 2010 20th International Conference on Pattern Recognition.

[29]  Frans Coenen,et al.  Graph-based Image Classification by Weighting Scheme , 2008, SGAI Conf..

[30]  Joost N. Kok,et al.  Frequent subgraph miners: runtimes don't say everything , 2006 .

[31]  Frans Coenen,et al.  Text Classification using Graph Mining-based Feature Extraction , 2010, SGAI Conf..

[32]  Kaspar Riesen,et al.  Towards the unification of structural and statistical pattern recognition , 2012, Pattern Recognit. Lett..

[33]  José Francisco Martínez Trinidad,et al.  Full duplicate candidate pruning for frequent connected subgraph mining , 2010, Integr. Comput. Aided Eng..

[34]  José Francisco Martínez Trinidad,et al.  CAR-NF: A classifier based on specific rules with high netconf , 2012, Intell. Data Anal..

[35]  Hui Xiong,et al.  Mining globally distributed frequent subgraphs in a single labeled graph , 2009, Data Knowl. Eng..

[36]  Wei Wang,et al.  Comparing Graph Representations of Protein Structure for Mining Family-Specific Residue-Based Packing Motifs , 2005, J. Comput. Biol..

[37]  Kaspar Riesen,et al.  IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning , 2008, SSPR/SPR.

[38]  François Fouss,et al.  Graph nodes clustering with the sigmoid commute-time kernel: A comparative study , 2009, Data Knowl. Eng..

[39]  Joost N. Kok,et al.  A quickstart in frequent structure mining can make a difference , 2004, KDD.

[40]  S. V. S. Santhi,et al.  A Survey of Frequent Subgraph Mining algorithms for Uncertain Graph Data , 2015 .