A taxonomy of Self-organizing Maps for temporal sequence processing

Abstract

This paper presents a taxonomy for Self-organizing Maps (SOMs) for temporal sequence processing. Four main application areas for SOMs with temporal processing have been identified. These are prediction, control, monitoring and data mining. Three main techniques have been used to model temporal relations in SOMs: 1) pre-processing or post-processing the data, but keeping the basic SOM algorithm; 2) modifying the activation and/or learning algorithm to take those temporal dependencies into account; 3) modifying the network topology, either by introducing feedback elements, or by using hierarchical SOMs. Each of these techniques is explained and discussed, and a more detailed taxonomy is proposed. Finally, a list of some of the existing and relevant papers in this area is presented, and the distinct approaches of SOMs for temporal sequence processing are classified into the proposed taxonomy. In order to handle complex domains, several of the adaptation forms are often combined.