Unsupervised clustering of symbol strings and context recognition

Abstract

The representation of information based on symbol strings has been applied to the recognition of context. A framework for approaching the context recognition problem has been described and interpreted in terms of symbol string recognition. The symbol string clustering map (SCM) is introduced as an efficient algorithm for the unsupervised clustering and recognition of symbol string data. The SCM can be implemented in an online manner using a computationally simple similarity measure based on a weighted average. It is shown how measured sensor data can be processed by the SCM algorithm to learn, represent and distinguish different user contexts without any user input.