Frequent itemset mining is an exploratory data mining technique that has fruitfully been exploited to extract recurrent co-occurrences between data items. Since in many application contexts items are enriched with weights denoting their relative importance in the analyzed data, pushing item weights into the itemset mining process, i.e., mining weighted itemsets rather than traditional itemsets, is an appealing research direction. Although many efficient in-memory weighted itemset mining algorithms are available in literature, there is a lack of parallel and distributed solutions which are able to scale towards Big Weighted Data. This paper presents a scalable frequent weighted itemset mining algorithm based on the MapReduce paradigm. To demonstrate its actionability and scalability, the proposed algorithm was tested on a real Big dataset collecting approximately 34 millions of reviews of Amazon items. Weights indicate the ratings given by users to the purchased items. The mined itemsets represent combinations of items that were frequently bought together with an overall rating above average.

PaWI: Parallel Weighted Itemset Mining by means of MapReduce / Baralis, ELENA MARIA; Cagliero, Luca; Garza, Paolo; Grimaudo, Luigi. - STAMPA. - Proceedings of the 2015 IEEE International Congress on Big Data:(2015), pp. 25-32. ((Intervento presentato al convegno 2015 IEEE International Congress on Big Data tenutosi a New York (USA) nel 26-30 giugno 2015 [10.1109/BigDataCongress.2015.14].

PaWI: Parallel Weighted Itemset Mining by means of MapReduce

BARALIS, ELENA MARIA;CAGLIERO, LUCA;GARZA, PAOLO;GRIMAUDO, LUIGI
2015

Abstract

Frequent itemset mining is an exploratory data mining technique that has fruitfully been exploited to extract recurrent co-occurrences between data items. Since in many application contexts items are enriched with weights denoting their relative importance in the analyzed data, pushing item weights into the itemset mining process, i.e., mining weighted itemsets rather than traditional itemsets, is an appealing research direction. Although many efficient in-memory weighted itemset mining algorithms are available in literature, there is a lack of parallel and distributed solutions which are able to scale towards Big Weighted Data. This paper presents a scalable frequent weighted itemset mining algorithm based on the MapReduce paradigm. To demonstrate its actionability and scalability, the proposed algorithm was tested on a real Big dataset collecting approximately 34 millions of reviews of Amazon items. Weights indicate the ratings given by users to the purchased items. The mined itemsets represent combinations of items that were frequently bought together with an overall rating above average.
978-1-4673-7278-7
File in questo prodotto:
File Dimensione Formato  
PaWI.pdf

accesso aperto

Descrizione: Draft
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 111.71 kB
Formato Adobe PDF
111.71 kB Adobe PDF Visualizza/Apri
07207198.pdf

non disponibili

Descrizione: Versione editoriale
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 186.83 kB
Formato Adobe PDF
186.83 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

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/11583/2639847
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo