International Journal of Computer
& Organization Trends

Research Article | Open Access | Download PDF

Volume 2 | Issue 3 | Year 2012 | Article Id. IJCOT-V2I3P304 | DOI : https://doi.org/10.14445/22492593/IJCOT-V2I3P304

A Knowledge discovery Approach in Shopping Complex Database (ASCD)

K.Kavitha , Dr. E. Ramaraj

Citation :

K.Kavitha , Dr. E. Ramaraj, "A Knowledge discovery Approach in Shopping Complex Database (ASCD)," International Journal of Computer & Organization Trends (IJCOT), vol. 2, no. 3, pp. 13-16, 2012. Crossref, https://doi.org/10.14445/22492593/ IJCOT-V2I3P304

Abstract

Data mining and Knowledge Discovery (KD) has been widely accepted as a key technology for enterprises to improve their abilities in data analysis, decision support and the automatic extraction of knowledge from data. Existing method has framed the process of information extraction and also referred to as the knowledge discovery process as a series of strategic search decisions, subject to constraints with the objective of attaining a sufficient level of domain specific knowledge for use in enterprise planning. Prediction in financial domains is absolutely difficult for a number of reasons. Existing theories tend to be weak or non-existent, which makes problem formulation open, ended by forcing us to consider a large number of independent variables and thereby increasing the dimensionality of the search space. In this paper we are providing an effective association rule mining for developing the super market industries. We hope this would be the great help to them.

Keywords

Data mining, Knowledge Discovery, Decision Making.

References

[1] P. Bollmann-Sdorra, A.M. Hafez and V.V. Raghavan, “A Theoretical Framework for Association Mining based on the Boolean Retrieval Model,” DaWaK 2001, September 2001.
[2] A.M. Hafez, “Association mining of dependency between time series,” Proceedings of SPIE Vol. 4384, SPIE AeroSense, April 2001.
[3] V.V. Raghavan and A.M. Hafez, “Dynamic Data Mining,“ IEA/AIE 2000, pp.220-229, 2000.
[4] A.K. Tung, J. Han, L.V. Lakshmanan and R.T. Ng, "Constraint-based Clustering in Large Databases," Proc. Int. Conf. on Database Theory, pp. 405-419, 2001.
[5] E Turban, 1997. Decision support systems and expertsystems (Fifth Edition), Prentice-Hall, London: UK.
[6] HL Viktor, 1999. Learning by Cooperation: An Approach to Rule Induction and Knowledge Fusion, PhD dissertation, Department of Computer Science, University of Stellenbosch, Stellenbosch: South Africa.
[7] Abraham Bernstein, Shawndra Hill, and Foster Provost. An Intelligent Assistant for the Knowledge Discovery Process. Technical Report IS02-02, New York University, Leonard Stern School of Business, 2002.
[8] Joerg-Uwe Kietz, Regina Zuecker, Anna Fiammengo, and Giuseppe Beccari. Data Sets, Meta-data and Preprocessing Operators at Swiss Life and CSELT. Deliverable D6.2, IST Project MiningMart, IST-11993, 2000.
[9] Pavel Brazdil. Data Transformation and Model Selection by Experimentation and Meta-Learning. In C.Giraud-Carrier and M. Hilario, editors, Workshop Notes – Upgrading Learning to the Meta-Level: Model Selection and Data Transformation, number CSR-98-02 in Technical Report, pages 11–17. Technical University Chemnitz, April 1998.