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State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm

by Neelamadhab Padhy, M. Kannan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 16
Year of Publication: 2013
Authors: Neelamadhab Padhy, M. Kannan
10.5120/10719-5485

Neelamadhab Padhy, M. Kannan . State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm. International Journal of Computer Applications. 64, 16 ( February 2013), 29-39. DOI=10.5120/10719-5485

@article{ 10.5120/10719-5485,
author = { Neelamadhab Padhy, M. Kannan },
title = { State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 16 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number16/10719-5485/ },
doi = { 10.5120/10719-5485 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:16:37.960244+05:30
%A Neelamadhab Padhy
%A M. Kannan
%T State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 16
%P 29-39
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this 21st century is completely called as the information science where the large organizations need useful knowledge. The data mining algorithms look for patterns in data. While most existing data mining approaches look for patterns in a single data table, multi-relational data mining (MRDM) approaches look for patterns that involve multiple tables (relations) from a relational database. The database consists of a collection of tables (a relational database). Records in each table represent parts, and individuals can be reconstructed by joining over the foreign key relations between the tables. To reduce the I/O cost, the data accessed together during extraction phase are to be clustered in the same disk block. This paper represents the index structure ,what we generally called as the Imine index structure . This structure can efficiently exploited by different item set extraction as well as this novel index structure is implemented by using FP-Growth and LCM V. 2 algorithms. Again in this paper we have focused that how the MRDM techniques are used in different approaches like classification, clustering ILP (Inductive Logic Program) etc.

References
  1. Heckerman, D. Bayesian networks for knowledge discovery. Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. AAAI/MIT Press, Cambridge, Mass. , 1996.
  2. C. J. C. Burges. A tutorial on support vector machines for pattern recognition. In Data Mining and Knowledge Discovery, 2:121–168, 1998.
  3. J. R. Quinlan. In C4. 5: Programs for Machine Learning. Morgan Kaufmann, 1993.
  4. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD'00), Dallas, Texas, May 2000
  5. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large Databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (SIGMOD'93), Washington, D. C. , May 1993
  6. J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In Proceedings of the 1993 European Conference on Machine Learning (ECML'93), Vienna, Austria, April 1993.
  7. S. Muggleton. Inverse entailment and progol. In New Generation Computing, Special issue on Inductive Logic Programming, 13:245–286, 1995.
  8. S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the First International Workshop on Algorithmic Learning Theory (ALT'90), Tokyo, Japan, October 1990.
  9. P. Domingos. Prospects and challenges for multi-relational data mining. In ACM SIGKDD Explorations Newsletter, 5(1):80–83, 2003.
  10. A. Mutlu,P. Senkul,Y. Kavuruchu " Improving the scalability of ILP-based multi-relational concept discovery system through parallelization (2012) published in Elsevier page no-352-368
  11. L. Dehaspe and L. De Raedt. Mining Association Rules in Multiple Relations. In Proceedings of the 7th International Workshop on Inductive Logic Programming (ILP'97), Prague, Czech, September 1997.
  12. L. Dehaspe and H. Toivonen. Discovery of relational association rules. In Relational Data Mining, Springer-Verlag, 2001
  13. S. Nijssen and J. N. Kok. Efficient frequent query discovery in Farmer. In Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'03), Cavtat-Dubrovnik, Croatia, September 2003.
  14. L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation ranking: bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.
  15. J. M. Kleinberg. Authoritative sources in a hyperlinked environment. In Journal of the ACM, 46(5):604–632, 1999.
  16. G. Jeh and J. Widom. SimRank: A measure of structural-context similarity. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'02), Edmonton, Alberta, Canada, August 2002.
  17. X. Yin, J. Han, and P. S. Yu. LinkClus: Efficient Clustering via Heterogeneous Semantic Links In Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB'06), Seoul, Korea, September 2006.
  18. T. M. Mitchell. In Machine Learning. McGraw Hill, 1997.
  19. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Pro- proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI'99), Stockholm, Sweden, July 1999.
  20. B. Taskar, E. Segal, and D. Koller. Probabilistic classification and clustering in relational data. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle Washington, August 2001.
  21. J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation," Proc. ACM SIGMOD, 2000.
  22. T. Uno, M. Kiyomi, and H. Arimura, "LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets," Proc. IEEE ICDM Workshop Frequent Itemsets Mining Implementations (FIMI), 2004.
  23. Dzeroski S: Multi Relational Data Mining . An introduction ACM SIGKDD Explorations News Letter, Vol-5, 2003, page Nos:--15.
  24. Dzeroski S,Lavarc N:Relational Data Mining ,Springer,2001
  25. Knobbe A. Multi Relational Data Mining,IOS Press,Amestardam,2006
  26. Knobbe A,D Haas,M. ,Seibes ?: A propositationalization and aggregates ,proceedings of the 5th PKDD,2001 ,page :277-288
  27. Krogel,M . A,Wrobel S,: Transformation-Based Learning using Multi-Relational Age-Generation ,LNAI,2001P. :142-155
  28. Appice, A. ; Ceci, M. ; Lanza, A. : "Discovery of spatial association rules in georeferenced census data: a relational mining approach". In Proceedings of Intelligent Data Analysis (2003)
  29. Yin, X. ; Han J. ; Yang J. ; Yu, P. S. : "CrossMine: Efficient Classification across Multiple Database Relations". In Proceedings of ICDE (2004)
  30. Yin, X. ; Han, J. ; Yang, J. : "Efficient Multi-relational Classification by Tuple ID Propagation". In the Workshop on Multi-relational Data Mining in with KDD (2003)
  31. Kantardzic M(2003) Data Mining : Concepts, Models ,Methods and Algorithms ,New Jersey :Willey
  32. Knobbe AJ(2004) Multirelational Data Mining Thesis(Ph. D),the Netherlans page No :130
  33. J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation," Proc. ACM SIGMOD, 2000.
  34. E. Baralis, T. Cerquitelli, and S. Chiusano, "Index Support for Frequent Itemsets Mining in a Relational DBMS," Proceedings 21st International Conference on Data Engineering (ICDE), pp. 754 - 765, 2005.
  36. Xin-Ye Li, Jin-Sha Yuan and Ying-Hui Kong, "Mining Association Rules from XML Data with Index Table," International Conference on Machine Learning and Cybernetics, Vol. 7, pp. 3905 – 3910, 2007
  37. E. J. Keogh and M. J. Pazzani, "An index-ing scheme for fast similarity search in large time series databases," Eleventh International Conference on Scientific and Statistical Database Management, pp. 56 – 67, 1999.
  38. R. E. Thevar and R. Krishnamoorthy, "A new approach of modified transaction reduction algorithm for mining frequent itemset," 11th International Conference on Computer and Information Technology (ICCIT 2008), pp. 1 – 6, 2008
  39. Neelamadhab Padhy and Rasmita Panigrahi "Multi Relational Data Mining Approach: A Data Mining Technique" published in International Journal of Computer Application(IJCA) in the month of Nov-2012
  40. H. Mannila, H. Toivonen, and A. I. Verkamo, "Efficient Algorithms for Discovering Association Rules," Proc. AAAI Workshop Knowledge Discovery in Databases (KDD '94), pp. 181-192, 1994.
  41. A. Savasere, E. Omiecinski, and S. B. Navathe, "An Efficient Algorithm for Mining Association Rules in Large Databases," Proc. 21st Int'l Conf. Very Large Data Bases (VLDB '95), pp. 432-444, 1995.
  42. . Y. G. Sucahyo and R. P. Gopalan, "CT-PRO: A Bottom Up Non Recursive Frequent Itemset Mining Algorithm Using Compressed FP-Tre Data Structure". In proc Paper presented at the IEEE ICDM Workshop on Frequent Itemset Mining Implementation (FIMI),Brighton UK, 2004.
  43. M. El-Hajj and O. R. Za¨?ane. Inverted matrix: Efficient discovery of frequent items in large datasets in the context f interactive mining. In Proc. 2003 Int'l Conf. on Data Mining and Knowledge Discovery (ACM SIGKDD), August 2003.
  44. M. El-Hajj and O. R. Za¨?ane: COFI-tree Mining:A New Approach to Pattern Growth with Reduced Candidacy Generation. Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations,19December2003,Melbourne, Florida,USA,CEURworkshop Proceedings, vol. 90 (2003)
Index Terms

Computer Science
Information Sciences

Keywords

Multi-relational Data Mining Association rules frequent item sets mining Structured Data Mining Rule mining Algorithm in MRDM(FP-Tree LCM V. 2 )

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