One of the main problems raising up in the frequent closed itemsets mining problem is the duplicate detection. In this paper we propose a general technique for promptly detecting and discarding duplicate closed itemsets, without the need of keeping in the main memory the whole set of closed patterns. Our approach can be exploited with substantial performance benefits by any algorithm that adopts a vertical representation of the dataset. We implemented our technique within a new depth-first closed itemsets mining algorithm. The experimental evaluation demonstrates that our algorithm outperforms other state of the art algorithms like CLOSET+ and FPCLOSE.
DCI Closed: a fast and memory efficient algorithm to mine frequent closed itemsets
Lucchese C;Orlando S;Perego R
2004
Abstract
One of the main problems raising up in the frequent closed itemsets mining problem is the duplicate detection. In this paper we propose a general technique for promptly detecting and discarding duplicate closed itemsets, without the need of keeping in the main memory the whole set of closed patterns. Our approach can be exploited with substantial performance benefits by any algorithm that adopts a vertical representation of the dataset. We implemented our technique within a new depth-first closed itemsets mining algorithm. The experimental evaluation demonstrates that our algorithm outperforms other state of the art algorithms like CLOSET+ and FPCLOSE.File | Dimensione | Formato | |
---|---|---|---|
prod_91394-doc_125385.pdf
solo utenti autorizzati
Descrizione: DCI Closed: a fast and memory efficient algorithm to mine frequent closed itemsets
Tipologia:
Versione Editoriale (PDF)
Dimensione
243.5 kB
Formato
Adobe PDF
|
243.5 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.