TY - CONF

T1 - Combining the Kohonen Self-Organizing Map and K-Means for On-Line Classification of Sensordata

AU - Van Laerhoven, Kristof

PY - 2001/1

Y1 - 2001/1

N2 - 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.

AB - 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.

KW - cs_eprint_id

KW - 394 cs_uid

KW - 1

M3 - Conference paper

SP - 464

EP - 470

T2 - ICANN 2001 : international conference on artificial neural networks

Y2 - 1 January 1900

ER -