A Technique to Mine the Multi-Relational Patterns Using Relational Tree and a Tree Pattern Mining Algorithm


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Abstract


The reason of the powerful application and large availability of the databases, the data mining become chalengable and trustful research field. The pattern mining is one of the fields in the data mining. The multi-relational data mining allows pattern mining from multiple table, in recent years multi-relational pattern mining is developing rapid manner. Even though the existing multi-relational mining algorithms are not ideal for the large amount of data. In this paper, we have developed a technique to mine the multi-realtional pattern using a relational tree. The relational tree constructed by reading the records from multi-relational database one by one and make the combination (relations) according the presence of fields in the database. From this we get the relational tree data structure without violating the relations of the records. Subsequently , the tree pattern mining algorithm is formulated and applied to the developed tree structure for mining the important relational patterns from the relational tree. The experimentation is carried out on the patient medical database and comparative results are extracted and the performance of the proposed approach is evaluated based on the existing work.
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Keywords


Relational Tree; Pattern Mining; Multi-Relational Database; Single Relation; Multi-Relation; Multi-Relation Data Mining

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References


Jun-Ichi Motoyama, Shinpei Urazawa, Tomofumi Nakano and Nobuhiro Inuzuka, "A Mining Algorithm Using Property Items Extracted from Sampled Examples, Inductive Logic Programming, pp. 335-350, 2007.

Nobuhiro Inuzuka and Toshiyuki Makino, "Implementing Multi-relational Mining with Relational Database Systems", 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 672 - 680, 2009.

Oded Z. Maimon, Lior Rokach, “Data mining and knowledge discovery handbook.” Springer 2005.

Saso Dzeroski, "Multi-relational data mining: an introduction", ACM SIGKDD Explorations Newsletter, COLUMN: Multi Relational Data Mining (MRDM), Vol. 5 , no. 1, pp. 1 - 16, July 2003.

J. Knobbe, H. Blockeel, A. Siebes, and Van der Wallen D. Multi-relational data mining. In Proceedings of Benelearn 99, 1999.

S. Dzeroski and N. Lavrac, editors. Relational Data Mining. Springer, Berlin, 2001.

S. Muggleton, editor. Inductive Logic Programming. Academic Press, London, 1992.

S. Dzeroski, L. De Raedt, and S. Wrobel, editors. Proceedings of the First International Workshop on Multi-Relational Data Mining. KDD-2002: Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, 2002.

Ullman, I.D. Principles of Databases and Knowledge-Based Systems, Volume I, Computer Science Press, 1988

Ullman, J., Widom, J., A First Course in Database Systems, Prentice Hall, 2001.

Date, C. An Introduction to Database Systems, Volume I, The Systems Programming Series, Addison-Wesley, 1986.

Yusuke Nakano and Nobuhiro Inuzuka, "Multi-Relational Pattern Mining Based-on Combination of Properties with Preserving Their Structure in Examples, In Proceedings of the 20th international conference on Inductive logic programming, pp. 181-189, 2010.

Siegfried Nijssen, A´ıda Jim´enez and Tias Guns, "Constraint-based Pattern Mining in Multi-Relational Databases", In Proceedings of the 11th IEEE International Conference on Data Mining, pp. 1120-1127, 2011.

Michelangelo Ceci and Annalisa Appice, “Spatial Associative Classification: Propositional vs Structural approach”, Journal of Intelligent Information Systems, vol. 27, no. 3, pp: 191- 213, November 2006.

Aída Jiménez, Fernando Berzal and Juan-Carlos Cubero, "Using trees to mine multirelational databases", Kluwer Academic Publishers Hingham, vol. 24, no. 1, pp. 1-39, 2012.

Chengqi Zhang, Jeffrey Xu Yu and Shichao Zhang, "Identifying Interesting Patterns in Multidatabases", Classification and Clustering for Knowledge Discovery, vol. 4, pp. 91-112, 2005.

Hector Ariel Leiva. A multi-relational decision tree learning algorithm. M.S. thesis. Deparment of Computer Science. Iowa State University, 2002.

Nobuhiro Inuzuka and Toshiyuki Makino, "Multi-Relational Pattern Mining System for General Database Systems", In Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems, pp. 72-80, 2010.

Jameson Mbale, Jackson Phiri and Tie Jun Zhao, "Relational Algebra Tree – Algorithm Data Mining (RAT-Adm): A System That Computes Query Optimization", International Journal of Data Engineering (IJDE), Vol. 1, no. 4, 2001.

A Collaborative Effort in Knowledge Discovery from Databases from http://lisp.vse.cz/pkdd99/Challenge/chall.htm

Seyyed Mohammad Saeed Tabatabaee, Ehsan Rafiee, Mohammad Jafar Abdi, Mohammad Reza Kangavari, Applying a Fuzzy Association Rule Mining Approach in the Robocup Soccer Simulation Domain, (2009) International Review on Computers and Software (IRECOS), 4 (1), pp. 133 - 141.

G. Yılmaz, B. Y. Badur, S. Mardikyan, Development of a Constraint based Sequential Pattern Mining Tool, (2011) International Review on Computers and Software (IRECOS), 6 (2), pp. 191-198.

Aloysius George, D. Binu, “DRL-PREFIXSPAN: A Novel Pattern Growth Algorithm for Discovering Downturn, Revision and Launch (DRL) Sequential Patterns”, Central European Journal of Computer Science, springer publication, vol. 2, no. 4, pp. 426-439, 2012.

Aloysius George, D. Binu, "An approach to products placement in supermarkets using PrefixSpan algorithm", Journal of King Saud University – Computer and Information Sciences, Elsevier publication, vol. 25, no. 1, pp. 77-87, 2012.


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