Knowledge-Driven Decision Support System Based on Knowledge Warehouse and Data Mining by Improving Apriori Algorithm with Fuzzy Logic

Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

Abstract:

In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: Decision support system, data mining, knowledge discovery, data discovery, fuzzy logic.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339201

References:

[1] Hamid R. Nemati, David M. Steiger, Lakshmi S. Iyer, Richard T. Herschel, "Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing", http://www.elsevier.com/locate/dsw, Decision Support Systems, Volume 33, pages 143– 161, 2002.
[2] Ahmad Bahgat El Seddawy1, Dr. Ayman Khedr2 and Prof. Dr. Turky Sultan, "Adapted Framework for Data Mining Technique to Improve Decision Support System in an Uncertain Situation", International Journal of Data Mining & Knowledge Management Process (IJDKP) Volume 2, Issue 3, Pages 1-9, May 2012.
[3] Hamid R. Nemati, David M. Steiger, Lakshmi S. Iyer, and Richard T. Herschel, "Knowledge Warehouse: An Architectural Integration of Knowledge Management, Decision Support, Data Mining and Data are housing", University of North Carolina at Greensboro, 2009.
[4] Mir Sajjad Hussain Talpur, Hina Shafi Chandio, Sher Muhammad Chandio, Hira Sajjad Talpur, "Knowledge Warehouse Framework", International Journal of Engineering Innovation & Research, ISSN: 2277 – 5668, Volume 1, Issue 3, Pages 262-270, 2012.
[5] Anthony Dymond, Dymond and Associates, LLC, Concord, CA, "The Knowledge Warehouse: The Next Step Beyond the Data Warehouse", Data Warehousing and Enterprise Solutions \ SUGI 27 \ Paper 144-27, 2008.
[6] Abdul-Aziz Rashid Al-Azmi, Kuwait University,"Data, Text, and Web Mining for Business Intelligence: A Survey", International Journal of Data Mining & Knowledge Management Process )IJDKP) Vol.3, No.2, March 2013.
[7] Yongjian Fu, "Data Mining: Tasks, Techniques, And Applications", Potentials, IEEE, ISSN 0278- 6648, Volume 16, Issue 4 Pages 18 - 20, Oct/Nov 1997. Daniel J. Power, Frada Burstein, and Ramesh Sharda, "Reflections on the Past and Future of Decision Support Systems: Perspective of Eleven Pioneers "\ chapter two, © Springer Science+Business Media, LLC, 2011.
[8] S. S Suresh, Prof. M. M. Naidu, S. Asha Kiran, "An XML Based Knowledge-Driven Decision Support System For Design Pattern Selection", International Journal of Research in Engineering and Technology (IJRET) ISSN 2277 –-4378 Vol. 1, No. 3, 2012.
[9] Michael Yacci, "The Knowledge Warehouse: Reusing Knowledge Components", ©Performance Improvement Quarterly \Volume 12, Issue 3, pages 132-140, September 1999, provider: citeseer 2008.
[10] Joseph M. Firestone, Ph.D. Executive Information Systems, "Knowledge Base Management Systems and The Knowledge Warehouse: A (Strawman)", http://www.dkms.com, [email protected], ©1999-2000 Executive Information Systems, Inc., Provider: citeseer 2009.
[11] Michael Goebel, Le Gruenwald, "A Survey of Data Mining and Knowledge Discovery Software Tools", SIGKDD Explorations. Copyright © 1999 ACM SIGKDD, June 1999, Volume 1, Issue 1, pages 20-33, provider: citeseer 2009.
[12] Max Bramer, the book "Principles of Data Mining", Printed on acid-free paper © Springer-Verlag London Limited 2007.
[13] CMPUT690, the book "Principles of Knowledge Discovery in Databases"\Chapter I: Introduction to Data Mining, © Osmar R. Zaïane, 1999.
[14] Rupali, Gaurav Gupta, "Data Mining: Techniques, Applications and Issues", International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), ISSN: 2277 – 9043, Volume 2, Issue, 2 February 2013.
[15] Slavco Velickov and Dimitri Solomatine, "Predictive Data Mining: Practical Examples", Artificial Intelligence in Civil Engineering. Proc. 2nd Joint, Workshop, Cottbus, Germany. ISBN 3-934934-00-5, March 2000.
[16] Radhakrishnan B, Shineraj G, Anver Muhammed K. M, "Application of Data Mining in Marketing", IJCSN International Journal of Computer Science and Network, ISSN (Online): 2277-5420 http://www.ijcsn.org, Volume 2 Issue 5, October 2013.
[17] Bezdek, J. C. (1998). Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers Norwell, MA, US.