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

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, Vienna.

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    Abstract

    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.

    Item Type: Conference or Workshop Item (Paper)
    Journal or Publication Title: ICANN 2001 : international conference on artificial neural networks
    Uncontrolled Keywords: cs_eprint_id ; 394 cs_uid ; 1
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 11931
    Deposited By: ep_importer_comp
    Deposited On: 25 Jun 2008 11:12
    Refereed?: Yes
    Published?: Published
    Last Modified: 17 Sep 2013 09:53
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/11931

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