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A survey on the development of self-organizing maps for unsupervised intrusion detection
This paper describes a focused literature survey of self-organizing maps (SOM) in support of
intrusion detection. Specifically, the SOM architecture can be divided into two categories, ie …
intrusion detection. Specifically, the SOM architecture can be divided into two categories, ie …
Binning sequences using very sparse labels within a metagenome
Background In metagenomic studies, a process called binning is necessary to assign
contigs that belong to multiple species to their respective phylogenetic groups. Most of the …
contigs that belong to multiple species to their respective phylogenetic groups. Most of the …
An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data
Abstract Motivation: Current Self-Organizing Maps (SOMs) approaches to gene expression
pattern clustering require the user to predefine the number of clusters likely to be expected …
pattern clustering require the user to predefine the number of clusters likely to be expected …
Using growing self‐organising maps to improve the binning process in environmental whole‐genome shotgun sequencing
Metagenomic projects using whole‐genome shotgun (WGS) sequencing produces many
unassembled DNA sequences and small contigs. The step of clustering these sequences …
unassembled DNA sequences and small contigs. The step of clustering these sequences …
Dimensionality reduction for visualizing high-dimensional biological data
High throughput technologies used in experimental biological sciences produce data with a
vast number of variables at a rapid pace, making large volumes of high-dimensional data …
vast number of variables at a rapid pace, making large volumes of high-dimensional data …
A directed batch growing approach to enhance the topology preservation of self-organizing map
The growing self-organizing map (GSOM) possesses effective capability to generate feature
maps and visualizing high-dimensional data without pre-determining their size. Most of the …
maps and visualizing high-dimensional data without pre-determining their size. Most of the …
Smart motion detection sensor based on video processing using self-organizing maps
Most current approaches to computer vision are based on expensive, high performance
hardware to meet the heavy computational requirements of the employed algorithms. These …
hardware to meet the heavy computational requirements of the employed algorithms. These …
Structure in the Three-dimensional galaxy distribution. I. Methods and example results
Three methods for detecting and characterizing structure in point data, such as that
generated by redshift surveys, are described: classification using self-organizing maps …
generated by redshift surveys, are described: classification using self-organizing maps …
[HTML][HTML] Large-scale map** of carbon stocks in riparian forests with self-organizing maps and the k-nearest-neighbor algorithm
Among the machine learning tools being used in recent years for environmental applications
such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm …
such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm …
Bregman divergences for growing hierarchical self-organizing networks
Growing hierarchical self-organizing models are characterized by the flexibility of their
structure, which can easily accomodate for complex input datasets. However, most …
structure, which can easily accomodate for complex input datasets. However, most …