Search
Skip to Search Results
Filter
Collections
Author / Creator / Contributor
Year
Languages
Item type
Departments
-
Spring 2024
In machine learning and data mining, outliers—data points significantly differing from the majority—often pose challenges by introducing irrelevant information. Unsupervised methods are often used for detecting them as the information about outliers is unknown. Global-Local Outlier Scores based...
-
High-dimensional data mining: subspace clustering, outlier detection and applications to classification
DownloadSpring 2010
Data mining in high dimensionality almost inevitably faces the consequences of increasing sparsity and declining differentiation between points. This is problematic because we usually exploit these differences for approaches such as clustering and outlier detection. In addition, the exponentially...
1 - 2 of 2