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Analysis and Comparative Study of Classifiers for Relational Data Mining

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 55 - Number 7
Year of Publication: 2012
Authors:
Vimalkumar B. Vaghela
Kalpesh H. Vandra
Nilesh K. Modi
10.5120/8765-2685

Vimalkumar B Vaghela, Kalpesh H Vandra and Nilesh K Modi. Article: Analysis and Comparative Study of Classifiers for Relational Data Mining. International Journal of Computer Applications 55(7):11-21, October 2012. Full text available. BibTeX

@article{key:article,
	author = {Vimalkumar B. Vaghela and Kalpesh H. Vandra and Nilesh K. Modi},
	title = {Article: Analysis and Comparative Study of Classifiers for Relational Data Mining},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {55},
	number = {7},
	pages = {11-21},
	month = {October},
	note = {Full text available}
}

Abstract

As an important task of relational database, relational classification can directly classify the data that involve multiple relations from a relational database and have more advantages than propositional data mining approaches. The information age has provided us with huge data repositories which cannot longer be analyzed manually. Most available existing data mining algorithms looks for pattern in a single relation. To classify data from relational database need of multi-relational classification arise which is used to analyze relational database and used to predict behavior and unknown pattern automatically which include business data, bioinformatics, pharmacology, web mining, credit card fraud detection, disease diagnosis system, computational biology, online retailers. In this paper, we present the several kinds of multi-relational classification methods including Inductive Logic Programming (ILP) based, Associative based multi-relational classification, Emerging Patterns based, Relational database based classification approaches and discuss each relational classification approaches, their characteristics, their comparisons and challenging issues in detail.

References

  • Appice, A. , Ceci M. , Malgieri C. , Maleraba D. "Discovering relational emerging patters", AI*AI 2007, LNCS (LNAI), Vol. 4733, 206-217, Springer, Heidelberg, 2007.
  • Arno J. Knobbe, Arno Siebes, Hendrik Blockeel, Daniël van der Wallen, "Multi-Relational Data Mining, using UML for ILP", PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery Springer-Verlag London, UK ©2000.
  • Atramentov, A. , Leiva, H. , and Honavar, V. "A Multirelational Decision Tree Learning Algorithm-Implementation and Experiments", ILP LNCS, Vol. 2835, pp. 38-56, 2003.
  • Atramentov, A. , Leiva, H. , and Honavar, V. "Experiments with MRDTL -- A Multi-relational Decision Tree Learning Algorithm", ILP LNCS, Vol. 2835, pp. 38-56, 2003.
  • Blockeel, H. 1998. "Top-down induction of first order logical decision trees", Artificial Intelligence Journal, vol. 101,pp. 285-297.
  • Blockeel H. , De Raedt L. , and Ramon J. "Top-down induction of logical decision trees". In Proc. 1998 Int. Conf. Machine Learning (ICML'98), Madison, WI, Aug. 1998.
  • C. L. Curotto ,N. F. F. Ebecken, H. Blockeel. "Multi-relational data mining in Microsoft SQL Server", In Seventh International Conference on Data, Text and Web Mining and their Business Applications, Prague, Tsjechie, July, 2006.
  • Ceci, M. , Appice, A. , Maleraba, D. "Emerging Pattern Based Classification in Relational Data Mining", DEXA 2008, LNCS, vol. 5181, pp. 283-296.
  • Ceci M. , Appice A. , and Malerba D. "Mr-SBC: a Multi-Relational Naive Bayes Classifier", in N. Lavrac, D. Gamberger, L. Todorovski & H. Blockeel (Eds. ), Knowledge Discovery in Databases PKDD 2003, Lecture Notes in Artificial Intelligence, 2838, 95-106, Springer, Berlin, Germany.
  • Dehaspe, L. , Raedt, "Mining Association Rules in Multiple Relations", In Proceedings of the ILP, Springer-Verlang, London UK, pp. 125-132, 1997.
  • Dzeroski, S. , Lavtac, N. 2001. eds, "Relational data mining", Berlin: Springer.
  • Emde, W. , Wettschereck, "Relational instance based learning", In Proceedings of the 13th Int. Conference on Machine Learning, Morgan Kaufmann, San Mateo, CA, 122-130, 1996.
  • Fan, H. , Ramamonanarao, K. "An efficient single scan algorithm for mining essential jumping emerging patterns for classification", In Pacific-Asia Conference on Knowledge Discovery and Data Mining , pp. 456-462, 2002.
  • Frank, R. , Moser, F. , Ester, M. "A Method for Multi-Relational Classification Using Single and Multi-Feature Aggregation Functions", In Proceedings of 11th European Conf. on PKDD, Springer, Verlag Berlin Heidelberg, 2007.
  • Getoor, L. , Friedman, N. , Koller, D. , and Pfeffer, A. 2001. "Learning Probabilistic Relational Models", pp. 307-355, Springer Verlage, New York.
  • Guo, JF. , Li, J. , Bian, WF. "An Efficient Relational Decision Tree Classification Algorithm", In proceedings of 3rd ICNC, vol. 3, 2007.
  • Gu,Y. , Liu, H. , He, J. "MrCAR: A Multi relational Classification Algorithm based on Association Rules", Int. Conf. on Web Information Systems and Mining, pp. 256- 260, 2009.
  • Guo, H. , Herna, L. , Viktor. "Multirelational classification: a multiple view approach", Knowl. Inf. Systems, vol. 17, pp. 287–312, Springer-Verlag London, 2008.
  • H, J. Liu,H. ,et at, "Selecting Effective Features and Relations For EfficientMulti-Relational Classification", Computational Intelligence, Vol 26, No. 3, 2010.
  • Han, J. , Kamber, M. 2007. Data Mining: Concepts and Techniques", 2nd Edition, Morgan Kaufmann.
  • Héctor Ariel Leiva , Shashi Gadia , Drena Dobbs, "MRDTL: A multi-relational decision tree learning algorithm (2002)" Proceedings of the 13th International Conference on Inductive Logic Programming (ILP 2003).
  • Hongyan Liu, Xiaoxin Yin, Jiawei Han, "An Efficient Multi-relational Naïve Bayesian Classifier Based on Semantic Relationship Graph", Proceeding MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining, Pages 39 – 48, ACM New York, NY, USA ©2005.
  • Hongyu Guo, Herna L. Viktor, "Mining relational databases with multi-view learning", ACM, DOI: 10. 1145/1090193. 1090197, 2005.
  • Jing-Feng Guo, Jing Li, Wei-Feng Bian, "An Efficient Relational Decision Tree Classification Algorithm", IEEE 2007, Natural Computation, ICNC 2007.
  • Jinyan Li1, Guozhu Dong, Kotagiri Ramamohanarao, "Instance-Based Classification by Emerging Patterns, Principles of Data Mining and Knowledge Discovery", Lecture Notes in Computer Science, 2000, Volume 1910/2000, 191-200, DOI: 10. 1007/3-540-45372-5_19.
  • Kirsten, M. , Wrobel, S. , Horvath, "Distance Based Approaches to Relational Learning and Clustering: Relational Data Mining", Morgan Kaufmann (2005) 6, pp. 213-232, springer, Heidelberg.
  • Koller, Pfeffer, A. 1998. "Probabilistic frame-based systems", In Proceedings of the 15th National Conference on Artificial Intelligence, pp. 580–587, Madison, WI.
  • Kramer, S. , Widmer, G. 2001. "Inducing Classification and Regression Tress in First Order Logic: Relational Data Mining", pp. 140-159, Springer.
  • Lappoon R. Tang, Raymond J. Mooney, and Prem Melville, "Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism", KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), pp. 107-121, Washington DC, August, 2003.
  • Leiva, HA. "A multi-relational decision tree learning algorithm", ISU-CS-TR, Lowa State University, pp. 02-12, 2002.
  • Li, J. , Dong, G. , Ramamohanarao, K. , Wong, L. "A new instance-based lazy discovery and classification system", Machine Learning, vol. 54, No. 2, pp0. 99-124, 2004.
  • Li, J. , Dong, Ramamohanarao, K. "DeEPs: Instancebased classification by emerging patterns", Technical Report, Dept of CSSE, University of Melbourne, 2000.
  • Michelangelo Ceci , Donato Malerba, "Mr-SBC: a Multi-Relational Naive Bayes Classifier (2003)" Knowledge Discovery in Databases PKDD 2003, Lecture Notes in Artificial Intelligence.
  • Muggleton, "Learning Stochastic Logic Programs", In Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, Technical Report WS-00-06, pp. 36-41.
  • Nijssen S. , Kok, J. "Faster Association Rules for Multiple Relations", In Proceedings of the IJCAI, pp. 891-896, 2001.
  • Pan Cao, Wang Hong-yuan. "Multi-relational classification on the basis of the attribute reduction twice", Communication and Computer,Vol. 6, No. 11. pp: 49-52, 2009.
  • Raymond J. Mooney, Prem Melville, Lappoon Rupert Tang, "Relational Data Mining with Inductive Logic Programming for Link Discovery",National Science Foundation Workshop on Next Generation Data Mining, Nov. 2002, Baltimore, MD.
  • Seda Daglar Toprak,Pinar Senkul, "A New ILP-based Concept Discovery Method for Business Intelligence", 2007, IEEE.
  • Taskar B, Segal E, Koller D, "Probabilistic Classification and Clustering in Relational Data", In Proceedings of International Conf. Artificial Intelligence, vol. 2, 2001.
  • V. B. Vaghela, A. Ganatra, and A. Thakkar. "Boost a weak learner to a strong learner using ensemble system approach". IEEE International Advance Computing Conference, 3:1432{1436, 2009.
  • Vimalkumar B. Vaghela, Dr. Kalpesh H. Vandra, Dr. Nilesh K. Modi, "Multi-Relational Classification Using Inductive Logic Programming", International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 3, May - 2012 ISSN: 2278-0181.
  • Wrobel S, "Inductive Logic Programming for Knowledge Discovery in Databases: Relational Data Mining", Berlin: Springer, pp. 74-101, 2001.
  • Xiaoxin Yin, Jiawei Han, Jiong Yang,et al. "Efficient Classification across Multiple Database Relations:A CrossMine Approach". IEEE Transactions on Knowledge and Data Engneering, 2006, 18 (6):770-783.
  • Xiuzhen Zhang , Guozu Dong , Ramamohanarao Kotagiri, "Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets", Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p. 310-314, August 20-23, 2000, Boston, Massachusetts, United States.
  • Yingqin Gu, Hongyan Liu, Jun He, Bo Hu and Xiaoyong Du, "MrCAR: A Multi-relational Classification Algorithm based on Association Rules", IEEE, Web Information Systems and Mining, 2009.
  • Yin X, Han J, and Yu PS, "CrossMine: Efficient Classification across Multiple Database Relations". In Proceedings of 20th Int. Conf. on Data Engineering (ICDE'04), 2004.
  • Yusuf Kavurucu, Pinar Senkul, Ismail Hakki Toroslu, "AGGREGATION IN CONFIDENCE-BASED CONCEPT DISCOVERY FOR MULTI-RELATIONAL DATA MINING", IADIS European Conference Data Mining 2008.
  • Zhang, X. , Dong, G. , Ramamohanarao, K. "Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets", In Proceedings of 6th SIGKDD international conference on Knowledge Discovery and Data Mining, pp. 310-314, 2000.