Open Access Open Access  Restricted Access Subscription or Fee Access

Advanced Partition Approach for Frequent Patterns Discovery

Pradeep Chouksey, Vijay Choudhary, Juhi Singh

Abstract


The discovery of association rules is the most well studied problem in data mining. It has received much attention among the various data mining problems. Many algorithms have been proposed for this purpose. In this paper an efficient algorithm for mining association rules has been proposed which is fundamentally different from known algorithms. Compared to previous algorithms this algorithm reduces the database scans thereby lowering the CPU overhead in most cases. We have performed extensive experiments and compared the performance of this algorithm with that of the best existing algorithm. It was found that the database scans are reduced in the proposed algorithm.


Keywords


Apriori, Association Rules, Frequent Patterns, Transactional Database.

Full Text:

PDF

References


Syed Khairuzzaman Tanbeer , Chowdhury , Farhan Ahmed and Byeong-Soo Jeong , Parallel and Distributed Algorithms for FP mining in large Databases, IETE Technical Review Vol 26, Issue 1 , pp 55-65, Jan 2009.

S.K. Tanbeer ,C.F. Ahmed, B-s Jeong and Y-K Lee, Efficient single pass frequent pattern mining using a prefix tree, Information Sciences Vol. 179 , Issue 5, pp. 559-583,2009.

Sanjeev sharma , Akhilesh Tiwari , Design of Algorithm for Frequent Patterns Discovery using Lattice Approch, ASIAN Journal of Information Management, Volume-I, pp. 11-18,2007.

Sotiris Kotsiantis , Dimitris Kanellopoulos, Association rule mining : A recent overview , GESTS International transactions on Computer Science and Engineering , Vol 32 (1), pp 71-82,2006.

Renata Ivancsy and Istvan Vajk, Fast Discovery of Frequent Itemsets : a cubic structure based Approach, Information 29, pp 71-78, 2005.

J. Han, J. Pei, J. Yin, Mining Frequent Patterns without Candidate Generation. In: Proc. Of Sigmod’00, pp. 1-12, 2000.

Hannu Toivanen , Sampling large databases for Association Rules ,Proceedings of the 22nd VLDB Conference Mumbai , India ,1996.

Ashok Savasere , Edward Omiecinski, Shamkant Navathe , An efficient algorithm for mining Association rules in large Databases.,Proceedings of 21st VLDB conference Zurich Switcherland.1995 .

Agarwal R., Imielinski T., and Swami A. Mining associations between sets of items in massive databases. In roceedings of the ACM SIGMOD International Conference on Management of Data, Washington D.C.,May 1993, pp. 207-216, 1993.

Margaret H. Dunham, “Data Mining: Introduction and Advanced Topics”, Pearson Education , 2005.

Jiawei Han (2004), Data Mining, Concepts and Techniques: San Francisco, CA: Morgan Kaufmann Publishers.

Margatet H. Dunham (2003). Data Mining, Introductory and Advanced Topics: Upper Saddle River, New Jersey: Pearson Education Inc.

Arun K Pujari (2003). Data Mining Techniques (Edition 5): Hyderabad,India: Universities Press (India) Private Limited.

Pieter Adriaans and Dolf Zantinge, “Data Mining”, Pearson Education,2001.

Margatet H. Dunham (2003). Data Mining, Introductory and Advanced Topics: Upper Saddle River, New Jersey: Pearson Education Inc.

Agrawal R., Mannila H., Srikanth R., Toivonen H.. Fast Discovery of association rules. Advances in Knowledge Discovery and Data Mining.Chapter 12, AAAI/MIT Press, 1995.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.