The week's pick
Reseach Article
A Proficient Approach of Incremental Algorithm for Frequent Pattern Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 48 - Number 20 |
| Year of Publication: 2012 |
| Authors: Endu Duneja, A.k. Sachan |
10.5120/7467-0596
|
Endu Duneja, A.k. Sachan . A Proficient Approach of Incremental Algorithm for Frequent Pattern Mining. International Journal of Computer Applications. 48, 20 ( June 2012), 36-40. DOI=10.5120/7467-0596
Abstract
The conundrum of mining association rules has drawn a lot of attention in the research community. In spite of their practical benefits, it is nontrivial to perform incremental mining or efficient mining of constrained association rules. Many researchers have recently focused on providing discrete solutions for these two problems. It is belief that constrained mining will be in tradition, incremental mining of constrained rules will be obligatory. In this paper, a novel algorithm for incremental mining is proposed which satiates the gap between incremental & constrained mining researchers. The proposed algorithm can discover sequential frequent pattern itemsets in incremental database. We developed new method that considers sequential data mining of marketing websites as an effective tool that participates in having well-structured websites. The advantage of this method is that is saves a lot maintenance efforts.
References
- Jiawei Han & Michelien Kamber, Edition 2003. "Data Mining: Concepts & Techniques", published by Elsevier, ISBN: 81-8147-049-4, pp 226-230
- Ayan N. F. , Tansel A. U & Arkun E. , 1999. "Ann efficient algorithm to update large itemsets with early pruning", In Proc. of 5th ACM SIGKDD international conference on Knowledge Discovery & Data Mining, CA USA, pp 23-29.
- D. Cheung, J. Han, C. Y. Wong, V. Ng, 1996. "Maintenance of discovered association Rules in large Databases: An Incremental Updating Technique", In Proc. of 1996 Int'l Conf. on Data Engg. (ICDE'96), USA.
- D. Cheung, S. D. Lee & Kao, 1997. "A general incremental technique for maintaining discovered association rules", In Proc. of Int'l Conf. on Database systems for adcanced Apllications, Australia, pp. 185-194.
- R. Feldman, Y. Aumann, A. Amir & H. Mannila, 1997. "Effeicient algorithms for discovering Frequent sets in Incremental databases", nI Proc. of SIGMOD Workshop on DMKD, Arizona.
- V. Ganti, J. E. Gehrke & Rmakrishanan, 2000. "Mining & monitoring evolving data", In Proc. of 16th International Conf. on Data Engg. , San Diego.
- D. Cheung, S. D. Lee, 1997. "Maintaenance of discovered association rules : When to update? ", In Proc. of ACm-SIGMOD workshop on DMKD, Tucson.
- E. Omiecinski, A. Savasere, 1998. "An efficient mining of association rules in large dynamic databases" In Proc. of BNCOD, pp. 13-24.
- N. L. Sarda & N. V. Srinivas, 1998. "An adaptive algorithm for incremental mining of association rules", In Proc. Of DEXA workshop, pp. 24-245.
- S. Thomas, S. Bodagala K. Alsabti & S. Ranka, 1997. "An efficient algorithm for incremental updation of association rules in large databases", In Proc. Of 3rd Int'l Conf. on KDD, CA.
- J. Bayardo, R. Agarwal & D. Gunoplulos, 1999. "Constraint based rule mining in large dense databases", In Proc. Of 15th Int'l Conf. on Data Engg. , pp188-197.
- P. Bardely, U. Fayyad & O. Mangasarian, 1998. "Data mining: Overview & optimization opportunities" Microsoft Research Report MSR-TR-98-04.
- L. V. S. Laxmanan, R. Ng. , J. Han & A. Pang, 1999. "Optimization of Constrained Frequent set queries with 2-variable constraints", In Proc. Of ACM-SIGMOD Conf. on Management of data, pp. 157-168.
- R. Srikant & R. Agarwal, 1997. "Mining association rules with item constraints", In Proc. Of 3rd Int'l Cf. on Knowledge Discovery in databases & Data Mining, pp. 67-73.
- J. Han & J. Pei & Y. Yin, 2000. "Mining frequent patterns without candidate generation" In Proc. of ACM-SIGMOD Int'l Conf. on Management of data, Dallas.
- M. Klemettinen, P. mannila & A. Verkamo, 1994. "Finding interesting rules from large sets of discovered association rules", In Proc. of 3rd Int'l Conf. on Information & Knowledge management, pp. 401-407.
- H. Toivonen, M. Klementtinen & K. Hatonen, 1995. "Pruning & grouping discovered association rules", In MLnet workshop on Statistics, machine Learning & discovery in Databases, pp. 47-52.
- C. Agarwal & P. H. Yu, 1998. "A new framework for itemset generation", In Proc. of 7th ACM-SIGACT-SIGMOD-SIGART Symposium on Principles of database systems, Washington.
- R. J. Bayardo & R. Agarwal, 1999. "Mining the most interesting rules", In Proc. of 5th ACM-SIGKDD Int'l Conf. on KDD, CA, USA.
- J. Han, L. V. S. Laxmanan, R. Ng. , 1999. "Constraint-based, Multidimensional data mining",IEEE Computer Special issue on data mining.
- R. T. Ng. , Lasshmanan, J. Han & A. Pang, 1998. "Exploratory mining & pruning optimizations of constrained association rules", in Proc. of ACM-SIG-MOD Int'l Conf. on Management of Data, pp. 13-24
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