Ubiquitous Self-Organizing Map: Learning Concept-Drifting Data Streams

Abstract

The Internet of Things promises a continuous flow of data where traditional database and data-mining methods cannot be applied. This paper presents a novel variant of the well-known Self-Organized Map (SOM), called Ubiquitous SOM (UbiSOM), that is being tailored for streaming environments. This approach allows ambient intelligence solutions using multidimensional clustering over a continuous data stream to provide continuous exploratory data analysis. The average quantization error over time is used for estimating the learning parameters, allowing the model to retain an indefinite plasticity and to cope with concept drift within a multidimensional stream.

Our experiments show that UbiSOM outperforms other SOM proposals in continuously modeling concept-drifting data streams, converging faster to stable models when the underlying distribution is stationary and reacting accordingly to the nature of the concept-drift in continuous real world data-streams.