The Kohonen self-organizing feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two dimensional form. This feature is used for classification and clustering of data. However, a great deal of e...

The kernel method has become a useful trick and has been widely applied to various learning models to extend their nonlinear approximation and classification capabilities. Such extensions have also recently occurred to the Self-Organising Map (SOM). In this paper, two recently proposed kernel SOMs are reviewed, together with their link to an energy function. The Self-Organising Mixture Network ...

We introduce a novel two-stage automatic XML mark-up system, which combines the WEBSOM approach to document categorisation in conjunction with the C5 inductive learning algorithm. The WEBSOM method clusters the XML marked-up documents such that semantically similar documents lie close together on a Self-Organising Map (SOM). The C5 algorithm automatically learns and applies mark-up rules derive...

A Web-based business always wants to have the ability to track users’ browsing behavior history. This ability can be achieved by using Web log mining technologies. In this paper, we introduce a Self-Organizing Map (SOM) based approach to mining Web log data. The SOM network maps the web pages into a two-dimensional map based on the users’ browsing history. Web pages with the similar browsing pa...

... ____________________________ _ 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 number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median inte...

We present an adaption of the self organizing map (SOM) useful for cluster analysis of large quantities of data such as music classification or customer behavior analysis. The algorithm is based on the batch SOM formulation which has been successfully adopted to other parallel architectures and perfectly suits the map reduce programming paradigm, thus enabling the use of large cloud computing i...

Self-organizing map (SOM) provides both clustering and visualization capabilities in mining data. Dynamic self-organizing maps such as Growing Self-organizing Map (GSOM) has been developed to overcome the problem of fixed structure in SOM to enable better representation of the discovered patterns. However, in mining large datasets or historical data the hierarchical structure of the data is als...

In this paper we present a comparison among some nonhierarchical and hierarchical clustering algorithms including SOM (Self-Organization Map) neural network and Fuzzy c-means methods. Data were simulated considering correlated and uncorrelated variables, nonoverlapping and overlapping clusters with and without outliers. A total of 2530 data sets were simulated. The results showed that Fuzzy c-m...

This paper has as aim the design and applications of two self-organizing maps using nonconventional metrics. First approach concerns the Levensthein Self-Organizing Map (LSOM). The LSOM is a SOM that uses a symbolic representation for both the input and also for the weight rows and it is based on the Levensthein metrics. The software implementation of the experimental LSOM model is designed for...

This paper aims to contribute in modeling and implementation, over a system on chip SoC, of a powerful technique for phonemes recognition in continuous speech. A neural model known by its efficiency in static data recognition, named SOM for self organization map, is developed into a recurrent model to incorporate the temporal aspect in these applications. The obtained model RSOM will subsequent...