Abstract
The growth in the new technology in the field of e-commerce and bioinformatics has resulted in production of large data sets with few new uniqueness. Microarray datasets consist of a very large number of features (nearly thousands of features) but very less number of rows because of its application type. ARM can be used to analyze such data and find the characteristics hidden in these data. However, most state-of-the-art ARM methods are not able to tackle a datasets containing large number of attributes effectively. In this paper, we have proposed and implemented a modified Carpenter algorithm with different consideration of data structure, which in result give us the better time complexity in compare to simple implementation of Carpenter.
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
Wright, A., McCoy, A., Henkin, S., Flaherty, M., Sittig, D.: Validation of an Association Rule Mining-Based Method to Infer Associations Between Medications and Problems. Ppl Clin. Inf. 4, 100–109 (2013)
Pan, F., Cong, G., Tung, A.K.H.: Carpenter: Finding closed patterns in long biological datasets. In: Proceedings of ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637–642 (2003)
Pei, J., Han, J., Mao, R.: CLOSET: An efficient algorithm for mining frequent closed item sets. In: Proceedings of ACM-SIGMOD International Workshop Data Mining and Knowledge Discovery, pp. 11–20 (2000)
Pan, F., Cong, G., Xin, X., Tung, A.K.H.: COBBLER: Combining Column and Row Enu-meration for Closed Pattern Discovery. In: International Conference on Scientific and Statistical Database Management, pp. 21–30 (2004)
Zaki, M., Hsiao, C.: Charm: An efficient algorithm for closed association rule mining. In: Proceedings of SDM, pp. 457–473 (2002)
Wang, J., Han, J., Pei, J.: Closet+: Searching for the best strategies for mining frequent closed item sets. In: Proceedings of 2003 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003)
Chen, E.S., Hripcsak, G., Xu, H., Markatou, M., Friedma, C.: Automated Acquisition of Disease: Drug Knowledge from Biomedical and Clinical Documents: An Initial Study. J. Am. Med. Inform. Assoc., 87–98 (2008)
Sim, S., Gopalkrishnan, V., Zimek, A., Cong, G.: A survey on enhanced subspace clustering. Data Mining Knowl. Disc. 26, 332–397 (2013)
Cheeseman, P.: Auto class: A Bayesian classification system. In: 5th International Conference on Machine Learning. Morgan Kaufmann (1988)
Associates, D.S.: The new direct marketing. Business One Irwin, Illinois (1990)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 1994 International Conference on Very Large Data Bases (VLDB 1994), pp. 487–499 (1994)
Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining frequent closed itemsets with counting inference. SIGKDD Explorations 2(2), 71–80 (2000)
Hongyan, L., Han, J.W.: Mining frequent Patterns from Very High Dimensional Data: A Top-down Row Enumeration Approach. In: Proceedings of the Sixth SIAM International Conference on Data Mining, pp. 20–22 (2006)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Proceedings of 7th International Conference on Database Theory, pp. 398–416 (1999)
Kriegel, H.P., Kröger, P., Zimek, A.: Clustering high-dimensional data: A survey on sub-space clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery from Data 3(1), 1–58 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Singh, B., Singh, R., Kushwaha, N., Vyas, O.P. (2014). An Efficient Approach for Discovering Closed Frequent Patterns in High Dimensional Data Sets. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-07353-8_60
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07352-1
Online ISBN: 978-3-319-07353-8
eBook Packages: EngineeringEngineering (R0)