Streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding window model for processing and mining data streams. However, for some applications, other stream processing models such as the time-fading model and the landmark model are more appropriate. In this paper, we propose mining algorithms that use (i) the time-fading model and (ii) the landmark model to discover frequent patterns from streams of uncertain data.

Discovering Frequent Patterns from Uncertain Data Streams with the Time-Fading and Landmark Models

CUZZOCREA A;
2013

Abstract

Streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding window model for processing and mining data streams. However, for some applications, other stream processing models such as the time-fading model and the landmark model are more appropriate. In this paper, we propose mining algorithms that use (i) the time-fading model and (ii) the landmark model to discover frequent patterns from streams of uncertain data.
Knowledge discovery
data mining techniques
data streams
frequent itemsets
probabilistic data
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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: http://hdl.handle.net/20.500.11770/312579
 Attenzione

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

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact