Abstract
Mining frequent pattern has been studied for a long time. There were many algorithms introduced and proved their efficiency. But most of them have to rebuild the frequent patterns every time when there are some changes (insert, update or delete) in dataset. Accumulated Frequent Pattern has been introduced recently. It updates existing frequent patterns when there are any changes. But the time complexity is so high. This paper introduces two ways to parallelize the Accumulated Frequent Pattern algorithm and reduce the time complexity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nguyen, T.-T., Nguyen, V.-L.H., Nguyen, P.-K.: Accumulated Frequent Pattern – ICTMF2012: The Third International Conference on Theoretical and Mathematical Foundations of Computer Science, Bali, Indonesia, December 1-2 (2012)
Appice, A., Ceci, M., Turi, A., Malerba, D.: A parallel, distributed algorithm for relational frequent pattern discovery from very large datasets. Intelligent Data Analysis 15, 69–88 (2011)
Niimi, A., Yamaguchi, T., Konishi, O.: Parallel Computing Method of Extraction of Frequent Occurrence Pattern of Sea Surface Temperature from Satellite Data. In: The Fifteenth International Symposium on Artufical Life Robotics (AROB 15th 2010), B-Con Plaza, Beppu, Oita, Japan, February 4-6 (2010)
Xiaoyun, C., Yanshan, H., Pengfei, C., Shengfa, M., Weiguo, S., Min, Y.: HPFP-Miner: A Novel Parallel Frequent Itemset Mining Algorithm. In: Fifth International Conference on Natural Computation, ICNC 2009, August 14-16 (2009)
Yen, S.-J., Wang, C.-K., Ouyang, L.-Y.: A Search Space Reduced Algorithm for Mining Frequent Patterns. Journal of Information Science and Engineering 28, 177–191 (2012)
Li, H., Zhang, N., Chen, Z.: A Simple but Effective Maximal Frequent Itemset Mining Algorithm over Streams. Journal of Software 7(1) (January 2012)
Sudhamathy, G., Venkateswaran, C.J.: An Efficient Hierarchical Frequent Pattern Analysis Approach for Web Usage Mining. International Journal of Computer Applications 43(15), 0975–8887 (2012)
Verma, G., Nanda, V.: Frequent Item set Generation by Parallel Preprocessing on Generalized Dataset. International Journal of Scientific & Engineering Research 3(4) (April 2012)
Nancy, P., Ramani, R.G.: Frequent Pattern Mining in Social Network Data (Facebook Application Data). European Journal of Scientific Research 79(4), 531–540 (2012) ISSN 1450-216X
Gupta, R., Satsangi, C.S.: An Efficient Range Partitioning Method for Finding Frequent Patterns from Huge Database. International Journal of Advanced Computer Research 2(2) (June 2012) (ISSN (print): 2249-7277 ISSN (online): 2277-7970)
Bhadane, C., Shah, K., Vispute, P.: An Efficient Parallel Approach for Frequent Itemset Mining of Incremental Data. International Journal of Scientific & Engineering Research 3(2) (February 2012)
Duneja, E., Sachan, A.K.: A Proficient Approach of Incremental Algorithm for Frequent Pattern Mining. International Journal of Computer Applications (0975 – 888) 48(20) (June 2012)
Miura, T., Okada, Y.: Extraction of Frequent Association Patterns Co-occurring across Multi-sequence Data. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2012, Hong Kong, March14-16 (2012)
Baskar, M.S.S.X., Dhanaseelan, F.R., Christopher, C.S.: FPU-Tree Frequent Pattern Updating Tree. International Journal of Advanced and Innovative Research 1(1) (June 2012)
Hafija, S., Murthy, J.V.R., Anuradha, Y., Sekhar, M.C.: Mining Frequent Patterns from Data streams using Dynamic DP-tree. International Journal of Computer Applications (0975 – 8887) 52(19) (August 2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, TT., Nguyen, BH., Nguyen, PK. (2013). Parallelizing the Improved Algorithm for Frequent Patterns Mining Problem. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-36546-1_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36545-4
Online ISBN: 978-3-642-36546-1
eBook Packages: Computer ScienceComputer Science (R0)