Articolo in rivista, 2007, ENG

Approximate mining of frequent patterns on streams

Silvestri C.; Orlando S.

Dipartimento di Informatica, Università Ca' Foscari di Venezia, Venice, Italy; CNR-ISTI, Pisa, Italy

Many critical applications, like intrusion detection or stock market analysis, require a nearly immediate result based on a continuous and infinite stream of data. In most cases finding an exact solution is not compatible with limited availability of resources and real time constraints, but an approximation of the exact result is enough for most purposes. This paper introduces a new algorithm for approximate mining of frequent itemsets from streams of transactions using a limited amount of memory. The proposed algorithm is based on the computation of frequent itemsets in recent data and an effective method for inferring the global support of previously infrequent itemsets. Both upper and lower bounds on the support of each pattern found are returned along with the interpolated support. An extensive experimental evaluation shows that AP_Stream, the proposed algorithm, yields a good approximation of the exact global result considering both the set of patterns found and their supports.

Intelligent data analysis 11 (1), pp. 49–73

Keywords

H.2.8 Database Applications, Data Mining, Stream mining

CNR authors

Orlando Salvatore

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 68399

Year: 2007

Type: Articolo in rivista

Creation: 2009-06-16 00:00:00.000

Last update: 2018-01-16 08:36:26.000

External links

OAI-PMH: Dublin Core

OAI-PMH: Mods

OAI-PMH: RDF

URL: http://dl.acm.org/citation.cfm?id=1367489.1367493

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:68399

ISI Web of Science (WOS): 000246425500004

Scopus: 2-s2.0-41849106846