Towards OLAP – Based Data Mining using Multidimensional Database and Fuzzy Decision Trees
Tiruveedula Gopi Krishna1, Mohamed Abdeldaiem Abdelhadi2, Sabahaldin A.Hussain3
1Tiruveedula Gopi Krishna, Research Scholor, Lecturer, Department of Computer Science, Arts and Science, Hoon, Al-Jufra,Sirte University, Libya India.
2Dr. Mohamed Abdeldaiem Abdelhadi, Research Scholor, Lecturer, Department of Computer Science, Arts and Science, Hoon, Al-Jufra,Sirte University, Libya India.
3Sabahaldin A. Hussain, Research Scholor, Lecturer, Department of Computer Science, Arts and Science, Hoon, Al-Jufra,Sirte University, Libya India.
Manuscript received on 10 November 2013 | Revised Manuscript received on 18 November 2013 | Manuscript Published on 30 November 2013 | PP: 22-24 | Volume-3 Issue-6, November 2013 | Retrieval Number: F1305113613/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, a new approach for data mining is described, coupling fuzzy multi-dimensional databases and fuzzy data mining systems, and achieving knowledge discovery from imperfect data. An architecture based on fuzzy multidimensional databases is given. It uses these kinds of data repositories to extract relevant knowledge from large data sets from the real world. According to several works that highlighted the great interest of the OLAP framework in the knowledge discovery process, this approach enhances the existing solutions. It provides a way to deal with data from the real world, and to apply flexible operations on data sets stored as multidimensional arrays, generating more understandable fuzzy rules. In recent works an extension of multidimensional database has been defined in order to handle imperfect information and flexible multidimensional queries.
Keywords: Olap, Fuzzy Rules, Multidimensional Database, Data Mining.
Scope of the Article: Fuzzy Logics