Distributed Data-Mining by Probability-Based Patterns

In this paper a new method is suggested for distributed data-mining by the probability patterns. These patterns use decision trees and decision graphs. The patterns are cared to be valid, novel, useful, and understandable. Considering a set of functions, the system reaches to a good pattern or better objectives. By using the suggested method we will be able to extract the useful information from massive and multi-relational data bases.




References:
[1] M. Karegar, A. Isazadeh, F. Fartash, T. Saderi, A. Habibizad Navin.
"Data-mining by the probability-based patterns," Published in the
proceeding of the 30th International Conference on Information
Technology Integrity, ITI 2008 IEEE. June 2008.
[2] M. Karegar, R. Mirmiran, F. Fartash, T. Saderi. "Risk-management by
probability-based patterns in data-mining," Published in the proceeding
of the International Conference on Information Technology Symposium
2008, ITSim 2008 IEEE. August 2008.
[3] Supatcharee Sirikulvadhana (2002), "Data Mining as A Financial
Auditing Tool," unpublished thesis (M.Sc) The Swedish School of
Economics and Business Administration.
[4] Sankar K. Pal and Pabitra Mitra (2004): "Pattern Recognition
Algorithms for Data Mining," Calcutta, CHAPMAN & HALL/CRC
[5] Zaki Mohammed J., Ching-Tien Ho (2000): "Large-Scale Parallel Data
Mining," Berlin, Springer.ch1,pp 1-2.
[6] Aflori C, Leon F. Efficient distributed data mining using intelligent
agents. Supported in part by the National University Research Council
under Grant AT no 66 / 2004.
[7] Piatetsky-Shapiro G, Djeraba C, Getoor L. "What are the grand
challenges for datamining?" KDD-2006 Panel Report, SIGKDD
Explorations, Volume 8, Issue 2.
[8] Alvarez J L, Mata J, Riquelme J C. "Data mining for the management of
software development process," International Journal of Software
Engineering and Knowledge Engineering, (1994) World Scientific
Publishing Company. p.3.
[9] McGrail A J, Gulski E, Groot E R S. "Data mining techniques to access
the condition of high voltage electrical plant," School of Electrical
Engineering, University of New South Wales, SYDNEY, NSW 2052,
AUSTRALIA, On behalf of WG 15.11 of Study Committee 15, 2002.
[10] Ordieres Meré J B, and Castej Limas M. "Data mining in industrial
processes," Actas del III Taller Nacional de Miner a de Datosy
Aprendizaje, TAMIDA2005. P. 60.
[11] Hand D J, Mannila H, Smyth P. "Principles of Data Mining (Adaptive
Computation and Machine Learning)," The MIT Press (August 2001);
Ch 6: models and patterns.
[12] Jennings, N., Sycara, K., Wooldridge, M. "A Roadmap of Agent
Research and Development, Autonomous Agents and Multi-Agent
Systems," 1:7-38, 1998.
[13] Park, B., Kargupta, H. "Distributed Data Mining: Algorithms, Systems,
and Applications," In the Handbook of Data Mining, N. Ye (ed.),
Lawrence Erlbaum Associates, pp: 341-358, 2003.
[14] Freitas, A.; Lavington, S. H. "Mining very large data bases with parallel
processing," Kluwer Academic Publishers The Netherlands, 1998.
[15] Danish Khan. "CAKE - Classifying, Associating & Knowledge
DiscovEry An Approach for Distributed Data Mining (DDM) Using
Parallel Data Mining Agents (PADMAs)," Published in the proceeding
of International Conference on Information Technology Integrity, ITI
2008 IEEE. 2008.
[16] Zhongfei Zhang, Ruofei Zhang (2009): "Multimedia Data Mining A
Systematic Introduction to Concepts and Theory," Boca Raton, CRC
Press.
[17] Tan. P, Steinbach M., and Kumar V (2005): Introduction to Data
Mining, Addison-Wesley, ch3.
[18] Herbert A.Edelstein (1999): Introduction to Data Mining and
Knowledge Discovery,Third Edition.U.S.A:Two Crows Corporation,
pp:8-9, 2005.
[19] Hillol Kargupta, Jiawei Han, Philip S. Yu, Rajeev Motwani, and Vipin
Kumar (2008): "Next Generation of Data Mining," CRC Press, ch8.pp:
155.
[20] Jie Ouyang Patel, N.Sethi, I.K. Chi-Square. "Test Based Decision Trees
Induction in Distributed Environment," Data Mining Workshops, 2008.
ICDMW'08. IEEE International Conference on Dec. 2008.