In this paper, we propose an efficient distributed fuzzy associative classification model based on the MapReduce paradigm. The learning algorithm first mines a set of fuzzy association classification rules by employing a distributed version of a fuzzy extension of the well-known FP-Growth algorithm. Then, it prunes this set by using three purposely adapted types of pruning. We implemented the distributed fuzzy associative classifier using the Hadoop framework. We show the scalability of our approach by carrying out a number of experiments on a real-world big dataset. In particular, we evaluate the achievable speedup on a small computer cluster, highlighting that the proposed approach allows handling big datasets even with modest hardware support.

A MapReduce-based fuzzy associative classifier for big data

MARCELLONI, FRANCESCO;SEGATORI, ARMANDO
2015-01-01

Abstract

In this paper, we propose an efficient distributed fuzzy associative classification model based on the MapReduce paradigm. The learning algorithm first mines a set of fuzzy association classification rules by employing a distributed version of a fuzzy extension of the well-known FP-Growth algorithm. Then, it prunes this set by using three purposely adapted types of pruning. We implemented the distributed fuzzy associative classifier using the Hadoop framework. We show the scalability of our approach by carrying out a number of experiments on a real-world big dataset. In particular, we evaluate the achievable speedup on a small computer cluster, highlighting that the proposed approach allows handling big datasets even with modest hardware support.
File in questo prodotto:
File Dimensione Formato  
Marcelloni_799473.pdf

accesso aperto

Tipologia: Documento in Pre-print
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 822.57 kB
Formato Adobe PDF
822.57 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/799473
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 17
  • ???jsp.display-item.citation.isi??? 5
social impact