Classified Data Organizing Maps: A Survey

A Survey of Self-Organizing Data Structures

Processing Symbolic Data With Self-Organizing Maps

Current research on machine learning and related algorithms is focused mainly on numeric paradigms. It is, however, widely supposed that intelligent behavior strongly relies on ability to manipulate symbols. In this paper we present on-line versions of self-organizing maps for symbol strings. The underlying key concepts are average and similarity, applied on strings. These concepts are easily d...

Exploring Forensic Data with Self-Organizing Maps

Ubiquitous Data-Mining with Self-Organizing Maps

Advances in technology are turning our mobile phones or PDAs into powerful computing devices capable of executing data mining algorithms. This paper discusses how the Self Organizing Map (SOM) algorithm can be adapted to a cooperating network of these devices. This approach opens the door to several new applications of data mining, including active data-collecting devices and ondemand knowledge...

Cartogram Data Projection for Self-Organizing Maps

Self-Organizing Maps (SOMs) are often visualized by applying Ultsch’s Unified Distance Matrix (U-Matrix) and labeling the cells of the 2-D grid with training data observations. Although powerful and the de facto standard visualization for SOMs, this does not provide for two key pieces of information when considering real world data mining applications: (a) While the U-Matrix indicates the locat...