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...

Large datasets can be analyzed through different linear and nonlinear methods. Most frequently used linear method is Principal Component Analysis (PCA) known also as EOF (Empirical Orthogonal Function) analysis, permitting both clustering and visualizing high-dimensional data items. However, many problems are nonlinear in nature, so, for analyzing such a problems some nonlinear methods will be ...

Self-organizing map (SOM), which is an orderly mapping technique, can convert complex, non-linear and high-dimensional data into simple, geometric and low-dimensional data that can easily be visualized. In data analysis techniques, the capability of SOM and K-means for clustering large-scale databases has already been confirmed. Although SOM and K-means have their superior features for cluster ...

Self-organization constitutes an important paradigm in machine learning with successful applications e.g. in dataand web-mining. Most approaches, however, have been proposed for processing data contained in a fixed and finite dimensional vector space. In this article, we will focus on extensions to more general data structures like sequences and tree structures. Various modifications of the sta...

---------------------------------------------Abstract Recently, security of the computer systems becomes an important problem. Almost all computers use the password mechanism for the user authentication. But password mechanism has many issues. In this paper, we propose a kind of biometrics authentication method using the timings of key strokes of an identical simple phrase for all users. For th...

We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map (H2SOM) featuring two extensions of the HSOM (hyperbolic SOM): (i) a hierarchically growing variant that allows for incremental training with an automated adaptation of lattice size to achieve a prescribed quantization error and (ii) an approximate best match search that utilizes the special structure of the hyperbolic latti...