Kontakt
Kako kupovati
Dostava
Savjetnik za kupnju
High-Dimensional Data Mining
Subspace Clustering, Outlier Detection and applications to classification
Engleski
138 b
30 dana za povrat kupljenih proizvoda
Kupci su kupili i
/
Meki uvez
9.41
€
/
Meki uvez
70.48
€
/
Meki uvez
18.42
€
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.
Informacije o knjizi
Engleski
Poklonite ovu knjigu još danas
To je jednostavno
1 Dodajte knjigu u košaricu i odaberite isporuku kao poklon 2 Zauzvrat ćemo vam poslati kupon 3 Knjiga dolazi na adresu poklonoprimcaMoglo bi vas zanimati i
/
Meki uvez
75.85
€
/
Meki uvez
17.11
€
/
Meki uvez
13.06
€
/
Tvrdi uvez
155.36
€
