One of the biggest problems facing practical applications of Self-Organising Maps (SOM) is their dependence on the learning rate, the size of the neighbourhood function and the decrease of these parameters as training progresses, all of which have to be selected without firm theoretical guidance. This paper introduces a simple modification to the SOM that completely eliminates the learning rate...

With so much modern music being so widely available both in electronic form and in more traditional physical formats, a great opportunity exists for the development of a general-purpose recognition and music classification system. We describe an ongoing investigation into the subject of musical recognition purely by the sonic content from a standard recording.

The Self-Organizing Map (SOM) [11, 15] is a method for mapping data relationships and distributions in high dimensions to lower dimensions, where they are easier to visualize and process. One of the problems with the SOM is that it needs an externally applied annealing scheme to learn mappings. There is no firm theoretical guidance for selecting annealing schemes and their parameters, something...

The self-organizing map algorithm has been used successfully in document organization. We now propose using the same algorithm for document retrieval. Moreover, we test the performance of the self-organizing map by replacing the linear Least Mean Squares adaptation rule with the marginal median. We present two implementations of the latter variant of the self-organizing map by either quantifyin...

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...

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...

An algorithm of evolving self-organizing map (ESOM) is proposed as a dynamic version of the Kohonen self-organizing map, where network structure is evolved in an on-line adaptive mode. Experiments have been carried out on some benchmark data sets as well as on macroeconomic data. Results show that ESOM is a good tool for clustering, data analysis, and visualisation.

In this article, we report our implementation and comparison of two text clustering techniques. One is based on Ward’s clustering and the other on Kohonen’s Self-organizing Maps. We have evaluated how closely clusters produced by a computer resemble those created by human experts. We have also measured the time that it takes for an expert to ‘‘clean up’’ the automatically produced clusters. The...