Email this article 
Open Access
Subscription or Fee Access
An Efficient Rule based Association Analysis for Business Data Base
Abstract
In this Business world, everything is made computerized to make the process efficiently and to improve the business. In Business, information is valuable and need to be maintained. To do this, the database can be useful. In that type of organization, the database is used as OLAP (Online Line Analytical Processing). i.e., the database maintains historical data about the organization. In this situation, the size of the database grows large. These kinds of database in which large volumes of data are stored are termed as Data Warehouse. Extracting the data from this data warehouse is termed as Data Mining. When the database size grows large, mining the data becomes time consuming. To reduce the delay, some characteristics are needed. One such characteristic is called Association Analysis. This Association Analysis is used to mine the data based upon the analysis result of the data. The analysis is made by proposing such techniques. In this paper, the association rule is created to mine the data from the large amount of data based upon some characteristics. This paper is proposed to implement on the E-commerce organization. In that kind of organization, the main purpose of the organization is to provide satisfaction for the upcoming user. It can be done by extraction of the data from the database is through the customer behavior. That is, the rule is developed to mine the data with respect to the target customer behavior, there by, the performance of the server is enhanced. Specifically if the client enters into the site, the server has to search for the previous request for that site that was made by the customers. If the server detects the previous request then the customer is provided with the response depending upon the previous transaction. With the help of the customer behavior, the association rule is created and the better response is given to them. Since the proposed method is implemented in disconnected Architecture, it gives fast response to the user. A snapshot about this technique is explained briefly in this paper with suitable algorithm.
Keywords
References
“Privacy Preserving Association Rule Mining in Vertically Partitioned Data”, by Jaideep Vaidya, Department of Computer Sciences, Purdue University, West Lafayette, Indiana, jsvaidya@cs.purdue.edu and Chris Clifton, Department of Computer Sciences, Purdue University, West Lafayette, Indiana, clifton@cs.purdue.edu,
“Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications“, by Sunita Sarawagi Shiby Thomas * Rakesh Agrawal, sunita@almaden.ibm.com sthomas@cise.ufl.edu ragrawalQaZmaden.ibm.com, IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120
“CHARM: An Efficient Algorithm for Closed Association Rule Mining”, Mohammed J. Zaki and Ching-Jui Hsiao, Computer Science Department, Rensselaer Polytechnic Institute, Troy NY 12180 fzaki,hsiaocg@cs.rpi.edu, http://www.cs.rpi.edu/_zaki
“Maintaining Data Privacy in Association Rule Mining”, by Shariq J. Rizvi, Computer Science & Engineering, Indian Institute of Tecnology, Mumbai and Jayant R. Haritsa, Database Systems Lab, SERC, Indian Institute of science, Bangalore.
“Efficient Adaptive Support Association Rule Mining for Recommender Systems” by Weiyang Lin, Sergio A. Alvarez and Carolina Ruiz, USA.
“Algorithms for Association Rule Mining – A General Survey and Comparison”, Jochen Hipp, Wilhelm SchickardInstitute, University of T¨ubingen, 72076 T¨ubingen, Germany, jochen.hipp@informatik., unituebingen.de, Ulrich G¨untzer, Wilhelm SchickardInstitute, University of T¨ubingen, 72076 T¨ubingen, Germany, guentzer@informatik., unituebingen.de, Gholamreza, Nakhaeizadeh, DaimlerChrysler AG, Research & Technology, Germany. Rheza.nakhaeizadeh@daimlerchrysler.com
“Constraint-Based Rule Mining in Large, Dense Databases”, Roberto J. Bayardo Jr., Rakesh Agrawal, Dimitrios Gunopulos, IBM Research Division, Almaden Research Center, 650 Harry Road, San Jose, California 95120
“Web Personalization expert with combining collaborative filtering and association rule mining technique”, by C.H. Lee, Y.H.Kim and P.K. Rhee.
“Effective Personalization Based on Association Rule Discovery from Web Usage Data”, Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa, Center for Web Intelligence, School of Computer Science, Telecommunication, and Information Systems,DePaul University, 243 S. Wabash Ave., Chicago, Illinois 60604, USA
U. Shardanand, P. Maes. Social information filtering: algorithms for automating “word of mouth.” In Proceedings of the ACM CHI Conference (CHI95), 1995.
B. M. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Analysis of recommender algorithms for e-commerce. In Proceedings of the 2nd ACM E-Commerce Conference (EC’00), October 2000, Minneapolis.
C. C. Aggarwal, J.L. Wof, P. S. Yu. A new method for similarity indexing for market data. In Proceedings of the ACM SIGMOD Conference, 1999.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl.Application of dimensionality reduction in recommender systems: a case study. In Proceedings of the WebKDD 2000 Workshop at the ACM SIGKKD 2000, Boston, August 2000.
. Srivastava, R. Cooley, M. Deshpande, P-T. Tan. Web usage mining: discovery and applications of usage patterns from Web data. SIGKDD Explorations, 2, 2000
B. Mobasher, R. Cooley, and J. Srivastava. Creating adaptive web sites through usage-based clustering of urls. In IEEE Knowledge and Data Engineering Workshop (KDEX’99), November 1999.
B. Mobasher, R. Cooley, and J. Srivastava. Automatic personalization based on Web usage mining. In Communications of the ACM, (43) 8, August 2000.
B. Mobasher, H. Dai, T. Luo, M. Nakagawa, Y. Sun, and J. Wiltshire. Discovery of aggregate usage profiles for Web personalization. In Proceedings of the WebKDD 2000 Workshop at the ACM SIGKKD 2000, Boston, August 2000.
B. Mobasher, H. Dai, T. Luo and M. Nakagawa. Improving the effectiveness of collaborative filtering on anonymous Web usage data. In Proc eedings of the IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization (ITWP01), August 2001, Seattle.
Internationl Business Machines. IBM Intelligent Miner User's Guide, Version 1 Release 1, SH12-6213-00 edition, July 1996.
R. Agrawal, A. Arning, T. Bollinger, M. Mehta, J. Shafer, and R. Srikant. The Quest Data Mining System. In Proc. of the 2nd Int? Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, August 1996.
J. Han, Y. Fu, K. Koperski, W. Wang, and 0. Zaiane. DMQL: A data mining query language for relational datbases. In Proc. of the 1996 SIGMOD workshop on research issues on data mining and knowledge discovery, Montreal, Canada, May 1996.
T. Imiehnski and H. Mannila. A database perspective on knowledge discovery. Communication of the ACM, 39(11):58-64, Nov 1996.
T. Imielinski, A. Virmani, and A. Abdulghani. Discovery Board Application Programming Interface and Query Language for Database Mining. In Proc. of the 2nd Int’l Conference on Knowledge Discovery and Data Mining, Portland, Oregon, August 1996.
R. Meo, G. Psaila, and S. Ceri. A new SQL like operator for mining association rules. In Proc. of the 22nd Int’l Conference on Very Large Databases, Bombay, India, Sep 1996
D. Tsur, S. Abiteboul, C. Clifton, R. Motwani, and S. Nestorov. Query flocks: A generalization of association rule mining. In SIGMOD, 1998. to appear.
R. Agrawal and K. Shim. Developing tightly-coupled data mining applications on a relational database system. In Proc. of the 2nd Int’l Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, August 1996.
R. Agrawal, H. M&la, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast Discovery of Association Rules. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, chapter 12, pages 307-328. AAAI/MIT Press, 1996.
K. Rajamani, B. Iyer, and A. Chaddha. Using DB/2’s object relational extensions for mining associations rules. Technical Report TR 03,690., Santa Teresa Laboratory, IBM Corporation, sept 1997.
B.Goethals,"Memory Issues in Frequent Pattern Mining,"in Proceedings of SAC'04. Nicosia, Cyprus: ACM, 2004.
B. Goethals, "Survey on Frequent Pattern Mining, "Vol. 2004, 2003.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.