Van Laerhoven, Kristof (2001) Combining the Kohonen Self-Organizing Map and K-Means for On-Line Classification of Sensordata. In: ICANN 2001 : international conference on artificial neural networks, 1900-01-01.
Many devices, like mobile phones, use contextual profiles like in the car or in a meeting to quickly switch between behaviors. Achieving automatic context detection, usually by analysis of small hardware sensors, is a fundamental problem in human-computer interaction. However, mapping the sensor data to a context is a difficult problem involving near real-time classification and training of patterns out of noisy sensor signals. This paper proposes an adaptive approach that uses a Kohonen Self-Organizing Map, augmented with on-line k-means clustering for classification of the incoming sensor data. Overwriting of prototypes on the map, especially during the untangling phase of the Self-Organizing Map, is avoided by a refined k-means clustering of labeled input vectors.
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