High-Dimensional Data Mining

Name: High-Dimensional Data Mining
Brand: VDM Verlag
SKU: 9783639362114
Price: 305.67 RON
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Subspace Clustering, Outlier Detection and applications to classification

Autor: Andrew Foss

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Editura: VDM Verlag

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Data mining in high dimensionality typically faces the consequences of increasing sparsity and decli...

Informații despre carte

Autor

Andrew Foss

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2011

Pagini

152

EAN

9783639362114

ISBN

363936211X

Enbook ID

06843096

Editura

VDM Verlag

Greutate

231

Dimensiuni

152 x 229 x 9

Descriere completă

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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High-Dimensional Data Mining

Subspace Clustering, Outlier Detection and applications to classification

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