@article{(Open Science Index):https://publications.waset.org/pdf/9376,
	  title     = {Role of Association Rule Mining in Numerical Data Analysis},
	  author    = {Sudhir Jagtap and  Kodge B. G. and  Shinde G. N. and  Devshette P. M},
	  country	= {},
	  institution	= {},
	  abstract     = {Numerical analysis naturally finds applications in all
fields of engineering and the physical sciences, but in the
21st century, the life sciences and even the arts have adopted
elements of scientific computations. The numerical data analysis
became key process in research and development of all the fields [6].
In this paper we have made an attempt to analyze the specified
numerical patterns with reference to the association rule mining
techniques with minimum confidence and minimum support mining
criteria. The extracted rules and analyzed results are graphically
demonstrated. Association rules are a simple but very useful form of
data mining that describe the probabilistic co-occurrence of certain
events within a database [7]. They were originally designed to
analyze market-basket data, in which the likelihood of items being
purchased together within the same transactions are analyzed.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {1},
	  year      = {2012},
	  pages     = {122 - 125},
	  ee        = {https://publications.waset.org/pdf/9376},
	  url   	= {https://publications.waset.org/vol/61},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 61, 2012},