Role of Association Rule Mining in Numerical Data Analysis
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.
[1] R. Agrawal; T. Imielinski; A. Swami: Mining Association
Rules Between Sets of Items in Large Databases", SIGMOD Conference
1993: 207-216
[2] Jochen Hipp, Ulrich G├╝ntzer, and Gholamreza Nakhaeizadeh.
Algorithms for association rule mining - A general survey and
comparison. SIGKDD Explorations, 2(2):1-58, 2000.
[3] Piatetsky-Shapiro, G. (1991), Discovery, analysis, and presentation of
strong rules, in G. Piatetsky-Shapiro & W. J. Frawley, eds, ÔÇÿKnowledge
Discovery in Databases-, AAAI/MIT Press, Cambridge, MA.
[4] Tan, Pang-Ning; Michael, Steinbach; Kumar, Vipin (2005). "Chapter 6.
Association Analysis: Basic Concepts and Algorithms". Introduction to
Data Mining. Addison-Wesley. ISBN 0321321367.
[5] http://www.b3intelligence.com/NumericalDataMining.html
[6] http://en.wikipedia.org/wiki/Numerical_analysis
[7] http://www.mathworks.com/matlabcentral/fileexchange/3016-armadadata-
mining-tool-version-1-4
[1] R. Agrawal; T. Imielinski; A. Swami: Mining Association
Rules Between Sets of Items in Large Databases", SIGMOD Conference
1993: 207-216
[2] Jochen Hipp, Ulrich G├╝ntzer, and Gholamreza Nakhaeizadeh.
Algorithms for association rule mining - A general survey and
comparison. SIGKDD Explorations, 2(2):1-58, 2000.
[3] Piatetsky-Shapiro, G. (1991), Discovery, analysis, and presentation of
strong rules, in G. Piatetsky-Shapiro & W. J. Frawley, eds, ÔÇÿKnowledge
Discovery in Databases-, AAAI/MIT Press, Cambridge, MA.
[4] Tan, Pang-Ning; Michael, Steinbach; Kumar, Vipin (2005). "Chapter 6.
Association Analysis: Basic Concepts and Algorithms". Introduction to
Data Mining. Addison-Wesley. ISBN 0321321367.
[5] http://www.b3intelligence.com/NumericalDataMining.html
[6] http://en.wikipedia.org/wiki/Numerical_analysis
[7] http://www.mathworks.com/matlabcentral/fileexchange/3016-armadadata-
mining-tool-version-1-4
@article{"International Journal of Information, Control and Computer Sciences:58442", author = "Sudhir Jagtap and Kodge B. G. and Shinde G. N. and Devshette P. M", title = "Role of Association Rule Mining in Numerical Data Analysis", 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.", keywords = "Numerical data analysis, Data Mining, Association
Rule Mining", volume = "6", number = "1", pages = "75-4", }