Indian Journal of Automation and Artificial Intelligence

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Applications of Synthesized Patterns in Multi Database Mining (MDM)


Affiliations
1 Arignar Anna Government Arts College, Namakkal-637002, India
2 Valluvar College of Science & Management, Karur-639006, India
 

The notion of Multi Database Mining has been recognised as an important area in data mining community for determining various novel patterns among item sets that co-occur frequently. This paper shows the kinds of High level patterns, Exceptional Patterns and Suggested patterns and their applications. For giving a comfortable and easy usage, we constructed the multi database mining designed by fusing local patterns and universal techniques. After designing with new fusion, it helps much and provides the company many advantages. In order to improve the performance of various patterns, many multi database mining techniques used which leads to take a fruitful decision in the interstate companies.

Keywords

Association Rule, Patterns, Local Patters, Synthesized Patterns.
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  • Xindong Wu, C. Zhang, S. Zhang. Database classification for multi-database mining. Information Systems. 2005; 8(3), 71-88.

  • T. Ramkumar, S. Hariharan, S. Selvamuthukumaran. A survey on mining multiple data sources. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2013; 3(1), 1-11.

  • A. Adhikari. Synthesizing global exceptional patterns in different data sources. Intelligent Systems Reference Library. 2012; 5(1), 265-280.

  • S. Zhang, C. Zhang, J.X. Yu. An efficient strategy for mining exceptions in multi- databases. Information Sciences. 2004; 165(1-2), 1-20.

  • A. Adhikari, P.R. Rao. Capturing association among items in a database. Data & Knowledge Engineering. 2008; 67(3), 430-443.

  • AnimeshAdhikari. Clustering local frequency items in multiple databases. Information sciences. 2013; 3(4), 221-241.

  • Zhang S., Wu X. Fundamentals of association rules in data mining and knowledge discovery. WIRESs Data Min Knowledge Discovery. 2011; 1(2), 97-116.

  • J. Han, J. Pei, Y. Yiwen. Mining frequent patterns without candidate generation. http://www.cs.sfu.ca/~jpei/publications/sigmod00.pdf. Date accessed: 2000.

  • J. Han, M. Kamber. Data mining concepts and techniques. 2nd (Edn), Morgan Kanufmann. 2006.

  • R. Suganthi, P. Kamalakannan. Analyzing stock market data using clustering algorithm. International Journal of Future Computer and Communication. 2015; 4(2), 1-4.


Abstract Views: 428

PDF Views: 182




  • Applications of Synthesized Patterns in Multi Database Mining (MDM)

Abstract Views: 428  |  PDF Views: 182

Authors

P. Kamalakannan
Arignar Anna Government Arts College, Namakkal-637002, India
R. Suganthi
Valluvar College of Science & Management, Karur-639006, India

Abstract


The notion of Multi Database Mining has been recognised as an important area in data mining community for determining various novel patterns among item sets that co-occur frequently. This paper shows the kinds of High level patterns, Exceptional Patterns and Suggested patterns and their applications. For giving a comfortable and easy usage, we constructed the multi database mining designed by fusing local patterns and universal techniques. After designing with new fusion, it helps much and provides the company many advantages. In order to improve the performance of various patterns, many multi database mining techniques used which leads to take a fruitful decision in the interstate companies.

Keywords


Association Rule, Patterns, Local Patters, Synthesized Patterns.

References