A BRIEF STUDY ON ALL SEQUENTIAL PATTERNS USING TIME EFFICIENT TECHNIQUE

Sujit R. Wakchaure

Abstract


The database utilized in the mining process typically contains massive amounts of information collected by computerized applications. We can't mention the natively computing-centric environments like web access logs in net applications. These databases further used as rich and reliable sources for data generation and verification. Meanwhile, the massive databases present challenges for effective approaches for knowledge discovery. We might categorize recent studies in frequent pattern mining into the invention of association rules and also the discovery of sequential patterns. Association discovery finds closely correlated sets in order that the presence of some elements during a frequent set will imply the presence of the remaining elements (in identical set). Sequential pattern discovery finds temporal associations in order that not solely closely correlated sets however additionally their relationships in time are uncovered. Mining of sequential patterns is to get all sequences with a user such least support. The objective of sequential patterns is to look out and to search the sequences that have bigger than or equivalent to an explicit user pre-specified support. In this paper we are studying some technique by which early candidate sequence pruning and search space partitioning will be feasible for effective mining of patterns likewise it will lessen the time utilization and space utilization for sequential pattern mining.

Index Terms : Sequential patterns, knowledge discovery, support and threshold, candidate sequence, projection technique.


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References


Agrawal, R., Imielminski, T., and Swami, A. Mining Association Rules Between Sets of Items in Large Databases, In Proc. SIGMOD Conference, (Washington D.C., USA, May 26-28, 1993) 207-216.

Mannila, H., Toivonen and H., Verkano, A.I. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1, 1 (1997), 259-289

Das., G., Lin, K.-I., Mannila, H., Renganathan, G., and Smyth, P. Rule Discovery from Time Series. In Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining (New York, USA, August 27-31, 1998), 16-22.

Harms, S. K., Deogun, J. and Tadesse, T. 2002. Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences. In Proc. 13th Int. Symp. on Methodologies for Intelligent Systems (Lyon, France, June 27-29, 2002), pp. .373-376.

R. Srikant and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” Proceedings of the 5th International Conference on Extending Database Technology, Avignon, France, pp. 3-17, 1996. (An extended version is the IBM Research Report RJ 9994)

J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal and M.-C. Hsu, “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-projected Pattern Growth,” Proceedings of 2001 International Conference on Data Engineering, pp. 215-224, 2001.

J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal and M.-C. Hsu, “FreeSpan: Frequent Pattern-projected Sequential Pattern Mining,” Proceedings of the 6th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 355-359, 2000.

H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen, and U. Dayal, “Multi-Dimensional Sequential Pattern Mining,” Proceedings of the 10th International Conference on Information and Knowledge Management, pp. 81-88, 2001.

J. Ayres, J. E. Gehrke, T. Yiu, and J. Flannick, “Sequential PAttern Mining Using Bitmaps,” Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada, July 2002.

S. Parthasarathy, M. J. Zaki, M. Ogihara, and S. Dwarkadas, “Incremental and Interactive Sequence Mining,” Proceedings of the 8th International Conference on Information and Knowledge Management, Kansas, Missouri, USA, pp. 251-258, Nov. 1999.

M. J. Zaki, “SPADE: An Efficient Algorithm for Mining Frequent Sequences,” Machine Learning Journal, Vol. 42, No. 1/2, pp. 31-60, 2001.

Dr P padmaja, P Naga Jyoti, m Bhargava “Recursive Prefix Suffix Pattern Detection Approach for Mining Sequential Patterns” IJCA September 2011

Peng Huang, “Improved Algorithm Based on Sequential Pattern Mining of Big Data Set” pp. 115-118, IEEE 2016.

NIZAR R. MABROUKEH and C. I. EZEIFE” A Taxonomy of Sequential Pattern Mining Algorithms” ACM Computing.

Rajesh Boghey, Shailendra Singh”, Sequential Pattern Mining: A Survey on Approaches” IEEE International Conference on Communication Systems and Network Technologies, pp.670-674, 2013.


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