Proposing an Efficient Method for Frequent Pattern Mining
Data mining, which is the exploration of
knowledge from the large set of data, generated as a result of
the various data processing activities. Frequent Pattern Mining
is a very important task in data mining. The previous
approaches applied to generate frequent set generally adopt
candidate generation and pruning techniques for the
satisfaction of the desired objective. This paper shows how
the different approaches achieve the objective of frequent
mining along with the complexities required to perform the
job. This paper will also look for hardware approach of cache
coherence to improve efficiency of the above process. The
process of data mining is helpful in generation of support
systems that can help in Management, Bioinformatics,
Biotechnology, Medical Science, Statistics, Mathematics,
Banking, Networking and other Computer related
applications. This paper proposes the use of both upward and
downward closure property for the extraction of frequent item
sets which reduces the total number of scans required for the
generation of Candidate Sets.
[1] R. Agrawal, T. Imielinski, and A.N. Swami, "Mining association rules
between sets of items in large databases," Proceedings of ACM
SIGMOD International Conference on Management of Data, ACM
Press, Washington DC, pp.207-216, May 1993.
[2] Mohammed J. Zaki, "Scalable Algorithms for Association Mining,"
IEEE Transactions on Knowledge and Data Engineering, vol.12, no. 3,
pp. 372-390, May/June 2000.
[3] J. Han, J. Pei, and Y. Yin,"Mining Frequent Patterns without Candidate
Generation," Proceedings of ACM SIGMOD International Conference
on Management of Data, ACM Press, Dallas, Texas, pp. 1-12, May
2000.
[4] J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, "Hmine: Hyper-
Structure Mining of Frequent Patterns in Large Databases," Proceedings
of IEEE International Conference on Data Mining, pp. 441-448, 2001.
[5] Pietracaprina, and D. Zandolin, "Mining Frequent Item sets Using
Patricia Tries," FIMI -03, Frequent Itemset Mining Implementations,
Proceedings of the ICDM 2003 Workshop on Frequent Item set Mining
Implementations, Melbourne, Florida, Dec. 2003.
[6] G. Grahne, and J. Zhu, "Efficiently using prefix-trees in mining frequent
itemsets," FIMI -03, Frequent Itemset Mining Implementations,
Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining
Implementations, Melbourne, Florida,December 2003.
[7] Doug Burdick, Manuel Calimlim, Jason Flannick, Johannes Gehrke,
"MAFIA: A Maximal Frequent Itemset Algorithm," IEEE Transactions
on Knowledge and Data Engineering, vol.17, no. 11, pp. 1490-1505,
Nov. 2005.
[8] M.J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, "New algorithms for
fast discovery of association rules," Proceedings of the Third
International Conference on Knowledge Discovery and Data Mining,
AAAI Press, pp. 283-286, 1997.
[9] P. Shenoy, J. R. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, and D.
Shah, "Turbo-charging vertical mining of large databases," Proceedings
of ACM SIGMOD Intnational Conference on Management of Data,
ACM Press, Dallas, Texas, pp. 22-23, May 2000.
[10] Burdick, M. Calimlim, and J. Gehrke, "MAFIA: a maximal frequent
item set algorithm for transactional databases," Proceedings of
International Conference on Data Engineering, Heidelberg, Germany,
pp. 443-452, April 2001.
[11] M.J. Zaki, and K. Gouda, "Fast vertical mining using diffsets,"
Proceedings of the Nineth ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, Washington,D.C., ACM Press,
New York, pp. 326-335, 2003.
[12] R. Agrawal, C. Aggarwal, and V. Prasad, "A Tree Projection Algorithm
for Generation of Frequent Item Sets," Parallel and Distributed
Computing, pp. 350-371, 2000.
[13] Amol Ghoting,Gregory Buehrer,Srinivasan Parthasarthy,Daehyun
Kim,Anthony Nguyen,Yen-Kuang Chen and Pradeep Dubey "Cacheconscious
Frequent Pattern Mining on a Modern Processor" Proceedings
of the 31st VLDB Conference,Trondheim,Norway,2005.
[14] Vaibhav Kant Singh and Vijay Shah "Minimizing Space Time
Complexity in Frequent Pattern Mining by Reducing Database Scanning
and Using Pattern Growth Method" To be appeared in Chhattisgarh
Journal of Science & Technology, Coming Volume ISSN 0973-7219.
[15] Vaibhav Kant Singh and Vinay Kumar Singh "Minimizing Space Time
Complexity by RSTDB a new method for Frequent Pattern Mining" To
be appeared in Proceeding of the First International Conference on
Intelligent Human Computer Interaction ,Allahabad,2009.
[1] R. Agrawal, T. Imielinski, and A.N. Swami, "Mining association rules
between sets of items in large databases," Proceedings of ACM
SIGMOD International Conference on Management of Data, ACM
Press, Washington DC, pp.207-216, May 1993.
[2] Mohammed J. Zaki, "Scalable Algorithms for Association Mining,"
IEEE Transactions on Knowledge and Data Engineering, vol.12, no. 3,
pp. 372-390, May/June 2000.
[3] J. Han, J. Pei, and Y. Yin,"Mining Frequent Patterns without Candidate
Generation," Proceedings of ACM SIGMOD International Conference
on Management of Data, ACM Press, Dallas, Texas, pp. 1-12, May
2000.
[4] J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, "Hmine: Hyper-
Structure Mining of Frequent Patterns in Large Databases," Proceedings
of IEEE International Conference on Data Mining, pp. 441-448, 2001.
[5] Pietracaprina, and D. Zandolin, "Mining Frequent Item sets Using
Patricia Tries," FIMI -03, Frequent Itemset Mining Implementations,
Proceedings of the ICDM 2003 Workshop on Frequent Item set Mining
Implementations, Melbourne, Florida, Dec. 2003.
[6] G. Grahne, and J. Zhu, "Efficiently using prefix-trees in mining frequent
itemsets," FIMI -03, Frequent Itemset Mining Implementations,
Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining
Implementations, Melbourne, Florida,December 2003.
[7] Doug Burdick, Manuel Calimlim, Jason Flannick, Johannes Gehrke,
"MAFIA: A Maximal Frequent Itemset Algorithm," IEEE Transactions
on Knowledge and Data Engineering, vol.17, no. 11, pp. 1490-1505,
Nov. 2005.
[8] M.J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, "New algorithms for
fast discovery of association rules," Proceedings of the Third
International Conference on Knowledge Discovery and Data Mining,
AAAI Press, pp. 283-286, 1997.
[9] P. Shenoy, J. R. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, and D.
Shah, "Turbo-charging vertical mining of large databases," Proceedings
of ACM SIGMOD Intnational Conference on Management of Data,
ACM Press, Dallas, Texas, pp. 22-23, May 2000.
[10] Burdick, M. Calimlim, and J. Gehrke, "MAFIA: a maximal frequent
item set algorithm for transactional databases," Proceedings of
International Conference on Data Engineering, Heidelberg, Germany,
pp. 443-452, April 2001.
[11] M.J. Zaki, and K. Gouda, "Fast vertical mining using diffsets,"
Proceedings of the Nineth ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, Washington,D.C., ACM Press,
New York, pp. 326-335, 2003.
[12] R. Agrawal, C. Aggarwal, and V. Prasad, "A Tree Projection Algorithm
for Generation of Frequent Item Sets," Parallel and Distributed
Computing, pp. 350-371, 2000.
[13] Amol Ghoting,Gregory Buehrer,Srinivasan Parthasarthy,Daehyun
Kim,Anthony Nguyen,Yen-Kuang Chen and Pradeep Dubey "Cacheconscious
Frequent Pattern Mining on a Modern Processor" Proceedings
of the 31st VLDB Conference,Trondheim,Norway,2005.
[14] Vaibhav Kant Singh and Vijay Shah "Minimizing Space Time
Complexity in Frequent Pattern Mining by Reducing Database Scanning
and Using Pattern Growth Method" To be appeared in Chhattisgarh
Journal of Science & Technology, Coming Volume ISSN 0973-7219.
[15] Vaibhav Kant Singh and Vinay Kumar Singh "Minimizing Space Time
Complexity by RSTDB a new method for Frequent Pattern Mining" To
be appeared in Proceeding of the First International Conference on
Intelligent Human Computer Interaction ,Allahabad,2009.
@article{"International Journal of Information, Control and Computer Sciences:61693", author = "Vaibhav Kant Singh and Vijay Shah and Yogendra Kumar Jain and Anupam Shukla and A.S. Thoke and Vinay KumarSingh and Chhaya Dule and Vivek Parganiha", title = "Proposing an Efficient Method for Frequent Pattern Mining", abstract = "Data mining, which is the exploration of
knowledge from the large set of data, generated as a result of
the various data processing activities. Frequent Pattern Mining
is a very important task in data mining. The previous
approaches applied to generate frequent set generally adopt
candidate generation and pruning techniques for the
satisfaction of the desired objective. This paper shows how
the different approaches achieve the objective of frequent
mining along with the complexities required to perform the
job. This paper will also look for hardware approach of cache
coherence to improve efficiency of the above process. The
process of data mining is helpful in generation of support
systems that can help in Management, Bioinformatics,
Biotechnology, Medical Science, Statistics, Mathematics,
Banking, Networking and other Computer related
applications. This paper proposes the use of both upward and
downward closure property for the extraction of frequent item
sets which reduces the total number of scans required for the
generation of Candidate Sets.", keywords = "Data Mining, Candidate Sets, Frequent Item set,Pruning.", volume = "2", number = "12", pages = "4213-6", }
