https://www.robots.ox.ac.uk/~davidc/pubs/NDreview2014.pdf

Review: A review of novelty detection

http://www.wi.hs-wismar.de/~cleve/vorl/projects/dm/ss13/SOTA/quellen/1-s2.0-0893608094900914-main.pdf

Growing cell structures—a self-organizing network for unsupervised and supervised learning

The self-organizing map (SOM) is a very popular unsupervised neural-network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical relations of the data. With our novel growing hierarchical SOM (GHSOM) we address both limitations. The GHSOM is an artificial neural-network model with hierarchical architecture composed of independent growing SOMs. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated. The benefits of this novel neural network are a problem-dependent architecture and the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.

The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

https://vision.unipv.it/IA2/aa2008-2009/A%20self-organising%20network%20that%20grows%20when%20required.pdf

A self-organising network that grows when required

The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

Ordinary feature maps with fully connected, fixed grid topology cannot properly reflect the structure of clusters in the input space. Incremental feature map algorithms, where nodes and connections are added to or deleted from the map according to the input distribution can overcome this problem. Such algorithms have been limited to maps that can be drawn in 2-D only in the case of two-dimensional input space. In the proposed approach, nodes are added incrementally to a regular two-dimensional grid, which is drawable at all times, irrespective of the dimensionality of the input space. The process results in a map that explicitly represents the cluster structure of the high-dimensional input. >

/pdf/incremental-grid-growing-encoding-high-dimensional-structure-54izgjzal6.pdf

J. Blackmore

Papers

Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map