Hostname: page-component-cb9f654ff-nr592 Total loading time: 0 Render date: 2025-08-20T09:55:03.216Z Has data issue: false hasContentIssue false
  • English
  • Français

Scaling up classification rule induction through parallel processing

Published online by Cambridge University Press:  26 November 2012

Frederic Stahl  and
Max Bramer
Frederic Stahl
Affiliation:
School of Systems Engineering, University of Reading, Whiteknights, Reading RG6 6AY, UK; e-mail: Frederic.T.Stahl@gmail.com
Max Bramer
Affiliation:
School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, PO1 3HE Portsmouth, UK; e-mail: Max.Bramer@port.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

Information

Type
Articles
Information
The Knowledge Engineering Review , Volume 28 , Issue 4 , December 2013 , pp. 451 - 478
Copyright
Copyright © Cambridge University Press 2012 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Berrar, D., Stahl, F., Silva, C. S. G., Rodrigues, J. R., Brito, R. M. M., Dubitzky, W. 2005. Towards data warehousing and mining of protein unfolding simulation data. Journal of Clinical Monitoring and Computing 19, 307–317.CrossRefGoogle ScholarPubMed
Bramer, M. A. 2000. Automatic induction of classification rules from examples using N-Prism. In Research and Development in Intelligent Systems XVI, Bramer, M. A., Macintosh, A. & Coenen, F. (eds). Springer-Verlag, 99–121.CrossRefGoogle Scholar
Bramer, M. A. 2002. An information-theoretic approach to the pre-pruning of classification rules. In Intelligent Information Processing, Musen, B. N. M. & Studer, R. (eds). Kluwer, 201–212.CrossRefGoogle Scholar
Bramer, M. A. 2005. Inducer: a public domain workbench for data mining. International Journal of Systems Science 36(14), 909–919.CrossRefGoogle Scholar
Bramer, M. A. 2007. Principles of Data Mining. Springer.Google Scholar
Breiman, L. 1996. Bagging predictors. Machine Learning 24(2), 123–140.CrossRefGoogle Scholar
Breiman, L. 2001. Random forests. Machine Learning 45(1), 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J. 1984. Classification and regression trees. Wadsworth Publishing Company.Google Scholar
Caragea, D., Silvescu, A., Honavar, V. 2003. Decision tree induction from distributed heterogeneous autonomous data sources. In Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 03). Springer-Verlag, 341–350.Google Scholar
Catlett, J. 1991. Megainduction: Machine Learning on Very Large Databases. Unpublished doctoral dissertation, University of Technology Sydney.Google Scholar
Cendrowska, J. 1987. PRISM: an algorithm for inducing modular rules. International Journal of Man–Machine Studies 27, 349–370.CrossRefGoogle Scholar
Chan, P., Stolfo, S. J. 1993a. Experiments on multistrategy learning by meta learning. In Proceedings of 2nd International Conference on Information and Knowledge Management, Washington, DC, United States, 314–323.CrossRefGoogle Scholar
Chan, P., Stolfo, S. J. 1993b. Meta-Learning for multi strategy and parallel learning. In Proceedings of 2nd International Workshop on Multistrategy Learning, Harpers Ferry, West Virginia United States, 150–165.Google Scholar
Clark, P., Niblett, T. 1989. The CN2 induction algorithm. Machine Learning 3(4), 261–283.CrossRefGoogle Scholar
Cohen, W. W. 1995. Fast effective rule induction. In Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann, 115–123.CrossRefGoogle Scholar
Erman, L. D., Hayes-Roth, F., Lesser, V. R., Reddy, D. R. 1980. The Hearsay-II Speech-Understanding system: integrating knowledge to resolve uncertainty. ACM Computing Surveys (CSUR) 12(2), 213–253.CrossRefGoogle Scholar
Freitas, A. 1998. A survey of parallel data mining. In Proceedings of the 2nd International Conference on the Practical Applications of Knowledge Discovery and Data Mining, London, 287–300.Google Scholar
Frey, L. J., Fisher, D. H. 1999. Modelling decision tree performance with the power law. In Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics, Fort Lauderdale, Florida, USA, 59–65.Google Scholar
Fuernkranz, J. 1998. Integrative windowing. Journal of Artificial Intelligence Research 8, 129–164.CrossRefGoogle Scholar
Goldberg, D. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.Google Scholar
GSC-II 2012. (http://tdc-www.harvard.edu/catalogs/gsc2.html).Google Scholar
Han, J., Kamber, M. 2001. Data Mining: Concepts and Techniques. Morgan Kaufmann.Google Scholar
Hillis, W., Steele, L. 1986. Data parallel algorithms. Communications of the ACM 29(12), 1170–1183.CrossRefGoogle Scholar
Ho, T. K. 1995. Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, Canada, 1, 278.Google Scholar
Hunt, E. B., Stone, P. J., Marin, J. 1966. Experiments in Induction. Academic Press.Google Scholar
Joshi, M., Karypis, G., Kumar, V. 1998. Scalparc: a new scalable and efficient parallel classification algorithm for mining large datasets. In Proceedings of the 1st Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing, IPPS/SPDP 1998, Orlando, Florida, 573–579.Google Scholar
Kargupta, H., Park, B. H., Hershberger, D., Johnson, E. 1999. Collective data mining: a new perspective toward distributed data analysis. In Advances in Distributed and Parallel Knowledge Discovery, Kargupta, H. & Chan, P. (eds). AAAI/MIT Press, 133–184.Google Scholar
Kerber, R. 1992. Chimerge: discretization of numeric attributes. In Proceedings of the AAAI, San Jose, California, 123–128.Google Scholar
Lippmann, R. P. 1988. An introduction to computing with neural nets. SIGARCH Computer Architecture News 16(1), 7–25.CrossRefGoogle Scholar
Metha, M., Agrawal, R., Rissanen, J. 1996. SLIQ: a fast scalable classifier for data mining. In Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology. Springer, 1057, 18–32.Google Scholar
Michalski, R. S. 1969. On the Quasi-Minimal solution of the general covering problem. In Proceedings of the 5th International Symposium on Information Processing, Bled, Yugoslavia, 125–128.Google Scholar
Minitab 2010. (http://www.minitab.com/).Google Scholar
Park, B., Kargupta, H. 2002. Distributed data mining: algorithms, systems and applications. In Data Mining Handbook. IEA, 341–358.Google Scholar
Provost, F. 2000. Distributed data mining: scaling up and beyond. In Advances in Distributed and Parallel Knowledge Discovery, Kargupta, H. & Chan, P. (eds). MIT Press, 3–27.Google Scholar
Provost, F., Hennessy, D. N. 1994. Distributed machine learning: scaling up with coarse-grained parallelism. In Proceedings of the 2nd International Conference on Intelligent Systems for Molecular Biology, Stanford, California, 340–347.Google Scholar
Provost, F., Hennessy, D. N. 1996. Scaling up: distributed machine learning with cooperation. In Proceedings of the 13th National Conference on Artificial Intelligence. AAAI Press, 74–79.Google Scholar
Provost, F., Jensen, D., Oates, T. 1999. Efficient progressive sampling. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM, 23–32.Google Scholar
Quinlan, R. J. 1979a. Discovering rules by induction from large collections of examples. In Expert Systems in the Micro-Electronic Age. Edinburgh University Press.Google Scholar
Quinlan, R. J. 1979b. Induction Over Large Databases. Michie, D. (ed.). Technical No. STAN-CS-739, Stanford University, 168–201.Google Scholar
Quinlan, R. J. 1983. Learning efficient classification procedures and their applications to chess endgames. In Machine Learning: An AI Approach, Michalski, R. S., Carbonell, J. G. & Mitchell, T. M. (eds). Morgan Kaufmann, 463–482.Google Scholar
Quinlan, R. J. 1986. Induction of decision trees. Machine Learning 1(1), 81–106.CrossRefGoogle Scholar
Quinlan, R. J. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann.Google Scholar
SAS/STAT 2010. (http://www.sas.com/).Google Scholar
Segal, M. R. 2004. Machine Learning Benchmarks and Random Forest Regression. Center for Bioinformatics & Molecular Biostatistics, University of California.Google Scholar
Shafer, J., Agrawal, R., Metha, M. 1996. SPRINT: a scalable parallel classifier for data mining. In Proceedings of the 22nd International Conference on Very Large Databases. Morgan Kaufmann, 544–555.Google Scholar
Shannon, C. E. 1948. A mathematical theory of communication. The Bell System Technical Journal 27.CrossRefGoogle Scholar
Sirvastava, A., Han, E., Kumar, V., Singh, V. 1999. Parallel formulations of Decision-Tree classification algorithms. Data Mining and Knowledge Discovery 3, 237–261.CrossRefGoogle Scholar
Smyth, P., Goodman, R. M. 1992. An information theoretic approach to rule induction from databases. Transactions on Knowledge and Data Engineering 4(4), 301–316.CrossRefGoogle Scholar
Stahl, F. 2009. Parallel Rule Induction. Unpublished doctoral dissertation, University of Portsmouth.Google Scholar
Stahl, F., Berrar, D., Silva, C. S. G., Rodrigues, J. R., Brito, R. M. M., Dubitzky, W. 2005. Grid warehousing of molecular dynamics protein unfolding data. In Proceedings of the 15th IEEE/ACM International Symposium on Cluster Computing and the Grid. IEEE/ACM, 496–503.Google Scholar
Stahl, F., Bramer, M., Adda, M. 2008. Parallel induction of modular classification rules. In Proceedings of SGAI Conference (p. lookup-lookup). Springer.CrossRefGoogle Scholar
Stahl, F., Bramer, M., Adda, M. 2009a. Parallel rule induction with information theoretic pre-pruning. In Proceedings of the SGAI Conference, 151–164.Google Scholar
Stahl, F., Bramer, M. A., Adda, M. 2009b. PMCRI: a parallel modular classification rule induction framework. In Proceedings of MLDM. Springer, 148–162.Google Scholar
Stahl, F., Bramer, M., Adda, M. 2010. J-PMCRI: a methodology for inducing pre-pruned modular classification rules. In Artificial Intelligence in Theory and Practice III, Bramer, M. A. (ed.). Springer, 47–56.CrossRefGoogle Scholar
Stankovski, V., Swain, M., Kravtsov, V., Niessen, T., Wegener, D., Roehm, M. 2008. Digging deep into the data mine with DataMiningGrid. IEEE Internet Computing 12, 69–76.CrossRefGoogle Scholar
Szalay, A. 1998. The Evolving Universe. ASSL 231.Google Scholar
Way, J., Smith, E. A. 1991. The evolution of synthetic aperture radar systems and their progression to the EOS SAR. IEEE Transactions on Geoscience and Remote Sensing 29(6), 962–985.CrossRefGoogle Scholar
Wirth, J., Catlett, J. 1988. Experiments on the costs and benefits of windowing in ID3. In Proceedings of the 5th International Conference on Machine Learning(ML-88). Morgan Kaufmann, 87–95.Google Scholar
Witten, I. H., Eibe, F. 1999. Data mining: practical machine learning tools and techniques with java implementations. Morgan Kaufmann.Google Scholar