ó-SCLOPE : clustering categorical streams using attribute selection
Data(s) |
19/08/2005
|
---|---|
Resumo |
Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose σ-SCLOPE, a novel algorithm based on SCLOPE’s intuitive observation about cluster histograms. Unlike SCLOPE however, our algorithm consumes less memory per window and has a better clustering runtime for the same data stream in a given window. This positions σ-SCLOPE as a more attractive option over SCLOPE if a minor lost of clustering accuracy is insignificant in the application.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer-Verlag |
Relação |
http://dro.deakin.edu.au/eserv/DU:30003315/n20051340.pdf http://dx.doi.org/10.1007/11552451_128 |
Direitos |
2005, Springer-Verlag Berlin Heidelberg |
Palavras-Chave | #computer science #algorithms #data storage equipment #attribute selection #clustering accuracy #data acquisition |
Tipo |
Journal Article |