The ubiquitous self-organizing map for non-stationary data streams

Bruno Silva, Nuno C. Marques

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The Internet of things promises a continuous flow of data where traditional database and data-mining methods cannot be applied. This paper presents improvements on the Ubiquitous Self-Organized Map (UbiSOM), a novel variant of the well-known Self-Organized Map (SOM), 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 and average neuron utility over time are proposed and used to estimating the learning parameters, allowing the model to retain an indefinite plasticity and to cope with changes within a multidimensional data stream. We perform parameter sensitivity analysis and our experiments show that UbiSOM outperforms existing proposals in continuously modeling possibly non-stationary data streams, converging faster to stable models when the underlying distribution is stationary and reacting accordingly to the nature of the change in continuous real world data streams.
Original languageEnglish
Article number27
JournalJournal of Big Data
Issue number1
Publication statusPublished - 31 Dec 2015


  • Self-organizing maps
  • Data streams
  • Non-stationary data
  • Clustering
  • Exploratory analysis
  • Sensor data


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