Abstract
Due to their time complexity, conventional clustering methods often cannot cope with large data sets like bibliographic data in a scientific library. We will present a method for clustering library documents according to usage histories that is based on the exploration of object sets using restricted random walks.
We will show that, given the particularities of the data, the time complexity of the algorithm is linear. For our application, the algorithm has proven to work well with more than one million objects, from the point of view of efficiency as well as with respect to cluster quality.
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Franke, M., Thede, A. (2005). Clustering of Large Document Sets with Restricted Random Walks on Usage Histories. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_46
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DOI: https://doi.org/10.1007/3-540-28084-7_46
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