Data streams are one of the most relevant new data sources, they refer to flows of data that come at a very high rate. The distinguishing feature in data streams analysis is that the focus is on transient relations. The present paper proposes a quantitative transformation of the binary attributes exploiting Multidimensional Correspondence Analysis (MCA) to describe the evolving association structures among attributes over different time-frames. The quantitative coding of the original attributes, in addition to synthesize information, make possible visualizations for different purposes, such as factorial maps, parallel co-ordinates and dendrograms.

Visual monitoring tool of association patterns in binarydata flows

IODICE D'ENZA, Alfonso
2007

Abstract

Data streams are one of the most relevant new data sources, they refer to flows of data that come at a very high rate. The distinguishing feature in data streams analysis is that the focus is on transient relations. The present paper proposes a quantitative transformation of the binary attributes exploiting Multidimensional Correspondence Analysis (MCA) to describe the evolving association structures among attributes over different time-frames. The quantitative coding of the original attributes, in addition to synthesize information, make possible visualizations for different purposes, such as factorial maps, parallel co-ordinates and dendrograms.
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/11580/19663
 Attenzione

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

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