Abstract
Data streams are continuous, unbounded usually come in high speed and have a data distribution which often changes with time. They can be analogous to water stream which keeps on flowing on one direction. Data stream is used in many application domains—one of it being the analysis of data in real time also known as streaming data analytics. They vary to a large extent in case of storing in tradition database which needs the database to be a static one. The store and analyze strategy is not applicable in case of streaming data analytics. In order to analyze, it is required to find a certain amount of associativity among the data stream. Such associativity among the data items leads to the generation of association rules. These association rules are an important class of methods of finding regularities/patterns in such data. Using association rule mining all the interesting correlation amongst the data can be used to derive. These relationships in the data items, can go on to a large extent in helping larger transactions records in case of making a decision or drawing a conclusion out of it. This paper makes use of Apriori algorithm in data streams which can discard the non-frequent set of data-items and can finally obtain the frequent itemsets from them.
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
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216, Washington, DC, May 1993
Han J., Pei J., Yin Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery (2003)
Troiano, L., Rodríguez-Muñiz, L.J., Ranilla, J., Díaz, I.: Interpretability of fuzzy association rules as means of discovering threats to privacy. Int. J. Comput. Math. 89, 325–333 (2012)
Troiano, L., Scibelli, G.: Mining frequent itemsets in data streams within a time horizon. Department of Engineering, University of Sannio, Viale Traiano (2013)
Lee, H., Lin, C.R., Chen, M.S.: Sliding-window filtering: an efficient algorithm for incremental mining. In: ACM 10th International Conference on Information and Knowledge Management (CIKM 01), 263–270 (2001)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Santiago, Chile, Sept 1994
Wu, B., Zhang, D., Lan, Q., Heng, J.: An efficient frequent patterns mining algorithm based on Apriori Algorithm and the FP-tree structure. In: Third International Conference on Convergence and Hybrid Information Technology (2008)
Szathmary, L., Napoli, A., Valtchev, P.: Towards rare itemset mining. In: Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’07), 305–312. IEEE Computer Society, Washington, DC, USA 305–312 (2007)
Mannila, H., Toivonen, H., Verkamo, I.: Efficient algorithms for discovering association rules. In: Proceedings of the AAAI Workshop on Knowledge Discovery in Databases (KDD’94), 181–192. AAAI Press, Seattle, Washington, USA (1994)
Li, H.-F., Lee, S.-Y.: Mining frequent itemsets over data streams using efficient window sliding techniques. Expert Syst. Appl. 36(2), 1466–1477 (2009)
Liao, S., Chu, P., Hsiao, P.Y.: Data mining techniques and applications—a decade review from 2000 to 2011. Expert Syst. Appl. 39, 11303–11311 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Nath, N.D., Meena, M.J., Syed Ibrahim, S.P. (2016). Mining Frequent Itemsets in Real Time. In: Vijayakumar, V., Neelanarayanan, V. (eds) Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’). Smart Innovation, Systems and Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-30348-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-30348-2_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30347-5
Online ISBN: 978-3-319-30348-2
eBook Packages: EngineeringEngineering (R0)