Identifying structures in large data sets raises a number of problems. On the one hand, many methods cannot be applied to larger data sets, while, on the other hand, the results are often hard to interpret. We address these problems by a novel three-stage approach. First, we compute a small representation of the input data using a self-organizing map. This reduces the amount of data and allows ...

The VideoSOM sytem is a tool for content-based video navigation based on a growing self-organizing map. Our interface allows the user to browse the video content using simultaneously several perspectives, temporal as well as content-based representations of the video. Combined with the interaction possibilities between them this allows for efficient searching of relevant information in video co...

This paper describes a data organization system and genuine content-addressable memory called the WEBSOM. It is a two-layer self-organizing map (SOM) architecture where documents become mapped as points on the upper map, in a geometric order that describes the similarity of their contents. By standard browsing tools one can select from the map subsets of documents that are most similar mutually...

This paper presents a technique for building complex and adaptive meshes for urban and architecturaldesign. The combination of a self-organizing map and cellular automata algorithms stands as a method forgenerating meshes otherwise static. This intends to be an auxiliary tool for the architect or the urban planner,improving control over large amounts of spatial information. The trad...

To solve the mapping problem for the mobile robots in the unknown environment, a dynamic growing self-organizing map with growing-threshold tuning automatically algorithm (DGSOMGT) based on Self-organizing Map is proposed. It introduces a value of spread factor to describe the changing process of the growing threshold dynamically. The method realizes the network structure growing by training th...

|Many of the properties of the well-known Koho-nen map algorithm are not easily derivable from its discrete formulation. For instance, the \projection" implemented by the map from a high dimensional input space to a lower dimensional map space must be properly regarded as a projection from a smooth manifold to a lattice and, in this framework , some of its property are not easily identiied. Thi...

| Self-organizing maps are a prominent unsuper-vised neural network model providing cluster analysis of high-dimensional input data. However, in spite of enhanced vi-sualization techniques for self-organizing maps, interpreting a trained map proves to be diicult because the features responsible for a speciic cluster assignment are not evident from the resulting map representation. In this paper...

Consider a quadratic rational self-map of the Riemann sphere such that one critical point is periodic of period 2, and the other critical point lies on the boundary of its immediate basin of attraction. We will give explicit topological models for all such maps. We also discuss the corresponding parameter picture. Stony Brook IMS Preprint #2007/1 February 2007