The Self-Organizing Map (SOM) can be used for forming overviews of multivariate data sets and for visualizing them on graphical map displays. Each map location represents certain kinds of data items and the value of a variable in the representations can be visualized in the corresponding locations on the map display. Such component plane displays contain all the information needed for interpret...

The self-organising map (SOM) has been successfully employed as a nonparametric method for dimensionality reduction and data visualisation. However, for visualisation the SOM requires a colouring scheme to imprint the distances between neurons so that the clustering and boundaries can be seen. Even though the distributions of the data and structures of the clusters are not faithfully portrayed ...

In the real world, the amount and the complexity of data increase from year to year. Therefore, it is important to classify various data exactly. In data mining, clustering is one of typical analysis techniques and is studied for many applications, such as a statement, a pattern recognition, an image analysis and so on. Then, the Self-Organizing Map (SOM) [1] has attracted attention for the stu...

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e g documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The perfo...

The Self-Organizing Map (SOM) algorithm is widely used for building topographic maps of data represented in a vectorial space, but it does not operate with dissimilarity data. Soft Topographic Map (STM) algorithm is an extension of SOM to arbitrary distance measures, and it creates a map using a set of units, organized in a rectangular lattice, defining data neighbourhood relationships. In the ...

Recent efforts to develop large-scale brain and neurocognitive architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason for this is that most conventional SOMs use a static encoding representation: each input pattern or sequence is effectively represented as a fixed point activation pattern in the map layer, something that is inconsistent...

Natural and artificial cognitive systems suffer from forgetting information. However, in natural systems forgetting is typically gradual whereas in artificial systems forgetting is often catastrophic. Catastrophic forgetting is also a problem for the Self-Organizing Map (SOM) when used as a semantic memory model in a continuous learning task in a nonstationary environment. Methods based on rehe...

The Self-Organizing Map (Kohonen, 1997) is an effective and a very popular tool for data clustering and visualization. With this method, the input samples are projected into a low dimension space while preserving their topology. The samples are described by a set of features. The input space is generally a high dimensional space Rd. 2D or 3D maps are very often used for visualization in a low d...

The basic SOM is indi erent to the ordering of the input patterns. Real data, however, is often sequential in nature thus context of a pattern may signi cantly in uence its correct interpretation. One simple SOM model that takes the context of a pattern into account is the Temporal K ohonen Map (TKM),which was modi ed into the Recurrent Self Organizing Map (RSOM). We sho w analytically and with...