In this paper we address the problem of multivariate outlier detection using the
(unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a …

The problem of detection of multidimensional outliers is a fundamental and important
problem in applied statistics. The unreliability of multivariate outlier detection techniques …

Presents an application of self-organizing maps as a method of clustering and data analysis.
It is called SOM Analysis. It has some advantages over the traditional clustering algorithms …

This invention relates to the field of data mining. More specifically, the present invention
relates to the detection of outliers within a large body of multi-dimensional data. 2 …

Outlier detection methods automatically identify instances that deviate from the majority of
the data. In this paper, we propose a novel approach for unsupervised outlier detection …

Outlier or anomaly detection is the process through which datum/data with different
properties from the rest of the data is/are identified. Their importance lies in their use in …

The problem of outlier detection is one of the most important issues in the field of analysis
due to its applicability in several famous problem domains, including intrusion detection …

The Self-Organizing Map (SOM: Kohonen (1984, 2001)) is a neuro-computational algorithm
to map high-dimensional data to a two-dimensional space through a competitive and …

Outlier detection has been extensively researched in the context of unsupervised learning.
But the learning results are not always satisfactory, which can be significantly improved …

High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …