Abstract
One of the major data mining tasks is to cluster similar data, because of its usefulness, providing means of summarizing large ammounts of raw data into handy information. Clustering data streams is particularly challenging, because of the constraints imposed when dealing with this kind of input. Here we report our work, in which it was investigated the use of WiSARD discriminators as primary data synthesizing units. An analysis of StreamWiSARD, a new sliding-window stream data clustering system, the benefits and the drawbacks of its use and a comparison to other approaches are all presented.
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Cardoso, D., De Gregorio, M., Lima, P., Gama, J., França, F. (2012). A Weightless Neural Network-Based Approach for Stream Data Clustering. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_40
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DOI: https://doi.org/10.1007/978-3-642-32639-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
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