Approximate Frequent Itemsets Mining on Data Streams Using Hashing and Lexicographic Order in Hardware

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Bustio L, Cumplido R, Hernández-León R, Bande JM, Letras M, Feregrino C
Publication year: 2017


Abstract


Frequent Itemsets Mining is a data mining technique that has been used to extract useful knowledge from datasets; And recently, from data streams. Data streams are an unbounded and infinite flow of data arriving at high rates; Data Mining approaches for Frequent Itemsets Mining can not be used straightforwardly. Finding an alternative to the discovery of frequent itemsets on data streams is an active research topic. This paper introduces the first hardware-based algorithm for search task. It uses the top-k frequent 1-itemets detection, hashing and the lexicographic order of received items. Experimental results demonstrates the viability of the proposed method.



How to cite

APA:
Bustio, L., Cumplido, R., Hernández-León, R., Bande, J.M., Letras, M., & Feregrino, C. (2017). Approximate Frequent Itemsets Mining on Data Streams Using Hashing and Lexicographic Order in Hardware.

MLA:
Bustio, Lazaro, et al. "Approximate Frequent Itemsets Mining on Data Streams Using Hashing and Lexicographic Order in Hardware." Proceedings of the 8th Latin American Symposium on Circuits and Systems (LASCAS) 2017.

BibTeX: 

Last updated on 2018-19-04 at 03:57