FSRM: A Fast Algorithm for Sequential Rule Mining

Authors

  • Paliwal A M.E. Student, Medicaps Institute of Technology & Management Indore (M.P.)
  • Dave S Asst. Professor , Medicaps Institute of Technology & Management Indore (M.P.)

Keywords:

computing and automation technologies, scalable mining algorithms

Abstract

Recent developments in computing and automation technologies have resulted in computerizing business and scientific applications in various areas. Turing the massive amounts of accumulated information into knowledge is attracting researchers in numerous domains as well as databases, machine learning, statistics, and so on. From the views of information researchers, the stress is on discovering meaningful patterns hidden in the massive data sets. Hence, a central issue for knowledge discovery in databases, additionally the main focus of this paper, is to develop economical and scalable mining algorithms as integrated tools for management systems.

References

Rakesh Agrawal, Swami, A., & T. Imielminski, 1993, Mining Association Rules Between Sets of Items in Large Databases, SIGMOD Conference, pp. 207-216

Rakesh Agrawal, & Ramakrishnan Srikant, 1995, Mining Sequential Patterns. Proc. Int. Conf. on Data Engineering, pp. 3-14.

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

King Ip Lin., Heikki Mannila, Gautam Das, Gopal Renganathan, & Padhraic Smyth, 1998. Rule Discovery from Time Series. Proc. 4th Int.

Conf. on Knowledge Discovery and Data Mining.

Liying Jiang & Jiternder S Deogun, 2005. Prediction Mining – An Approach to Mining Association Rules for Prediction. Proceeding of

RSFDGrC 2005 Conference, pp.98-108.

Hsieh, Y. L., Yang, D.-L. & Wu, J. 2006. Using Data Mining to Study Upstream and Downstream Causal Realtionship in Stock Market. Proc. 2006 Joint Conference on Information Sciences.

Harms, S. K., Deogun, J. & Tadesse, T.2002. Discovering Sequential Association Rules with Constraints and Time Lags in Multiple

Sequences. Proc. 13th Int. Symp. on Methodologies for Intelligent Systems, pp. .373-376.

Hamilton, H. J. & Karimi, K. 2005. The TIMERS II Algorithm for the Discovery of Causality. Proc. 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 744-750.

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Published

2024-02-26

How to Cite

Paliwal, A., & Dave, S. (2024). FSRM: A Fast Algorithm for Sequential Rule Mining. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(10), 1140–1142. Retrieved from https://ijact.in/index.php/j/article/view/201

Issue

Section

Original Research Article

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