Abstract
It has become increasingly important in mobile computing to be able to recognize the situation i.e. the context of a mobile device user, in order to enhance the effectiveness of human computer interaction. This paper describes an approach to the extraction of higher level contexts from the multidimensional low level context information, focusing on the analysis and comparison of clustering and segmentation behaviour of crisp versus fuzzy information. Context clustering is performed by using the k-means algorithm and segmentation by using a minimum-variance algorithm. The results indicate that fuzzy quantization is superior to the crisp quantization with k-means, yielding more consistent clustering. Segment borders found from the fuzzy data correspond better to the real context changes than those found from the crisp data.
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