Abstract
Frequent Itemset Mining is a popular data mining task with the aim of discovering frequently co-occurring items and, hence, correlations, hidden in data. Many attempts to apply this family of techniques to Big Data have been presented. Unfortunately, few implementations proved to efficiently scale to huge collections of information. This review presents a comparison of a carefully selected subset of the most efficient and scalable approaches. Focusing on Hadoop and Spark platforms, we consider not only the analysis dimensions typical of the data mining domain, but also criteria to be valued in the Big Data environment.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: OSDI 2004, p. 10 (2004)
Pang-Ning, T., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley (2006)
Borthakur, D.: The hadoop distributed file system: Architecture and design. Hadoop Project 11, 21 (2007)
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: NSDI 2012, p. 2 (2012)
The Apache Mahout machine learning library (2013). http://mahout.apache.org/
The Apache Spark scalable machine learning library (2013). https://spark.apache.org/mllib/
Li, H., Wang, Y., Zhang, D., Zhang, M., Chang, E.Y.: PFP: parallel fp-growth for query recommendation. In: RecSys 2008, pp. 107–114 (2008)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD 2000, pp. 1–12 (2000)
Moens, S., Aksehirli, E., Goethals, B.: Frequent itemset mining for big data. In: SML: BigData 2013 Workshop on Scalable Machine Learning. IEEE (2013)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, pp. 487–499 (1994)
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: KDD 1997, pp. 283–286. AAAI Press (1997)
Qiu, H., Gu, R., Yuan, C., Huang, Y.: YAFIM: a parallel frequent itemset mining algorithm with spark. In: IPDPSW 2014, pp. 1664–1671, May 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Apiletti, D., Garza, P., Pulvirenti, F. (2015). A Review of Scalable Approaches for Frequent Itemset Mining. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds) New Trends in Databases and Information Systems. ADBIS 2015. Communications in Computer and Information Science, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-23201-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-23201-0_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23200-3
Online ISBN: 978-3-319-23201-0
eBook Packages: Computer ScienceComputer Science (R0)