Kniha High-Dimensional Data Mining Andrew Foss

High-Dimensional Data Mining

Subspace Clustering, Outlier Detection and applications to classification

Autor: Andrew Foss
Jazyk: Angličtina
Vazba: Brožovaná
Vydavatel: VDM Verlag
Dostupnost: U nakladatele na objednávku
Odesíláme za 17-27 dnů
1 407
Data mining in high dimensionality typically faces the consequences of increasing sparsity and decli...

Informace o knize

Autor
Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2011
Stránek
152
EAN
9783639362114
ISBN
363936211X
Enbook ID
06843096
Vydavatel
Hmotnost
231
Rozměry
152 x 229 x 9

Kompletní popis

Data mining in high dimensionality typically faces the consequences of increasing sparsity and declining differentiation between points, while sparsity tends to increase false negatives. Here, the problem of solving high-dimensional problems using low-dimensional solutions is addressed. In clustering, we provide a new framework for finding candidate subspaces and the clusters within them using only two-dimensional clustering. It is robust to noise and handles overlapping clusters. In the field of outlier detection, several novel algorithms suited to high-dimensional data are presented.m These outperform state-of-the-art outlier detection algorithms in ranking outlierness for many datasets regardless of whether they contain rare classes or not. This approach can be a powerful means of classification for heavily overlapping classes given sufficiently high dimensionality. This is achieved solely due to the differences in variance among the classes. On some difficult datasets, this unsupervised approach yielded better separation than the very best supervised classifiers. This opens a new field in data mining, classification through differences in variance rather than spatial location.

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