Abstract
Researchers analyzing large (>100,000 objects) data sets with the methods of cluster analysis often face the problem of computational complexity of algorithms that sometimes makes it impossible to analyze in an acceptable time. Common solution of this problem is to use less computationally complex algorithms (like k-means), which in turn can in many cases give much worse results than for example algorithms using eigenvalues decomposition. In the article, the new algorithm from spectral clustering family is proposed and compared with other approaches.
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Dudek, A. (2021). Evaluation of Two-Step Spectral Clustering Algorithm for Large Untypical Data Sets. In: Jajuga, K., Najman, K., Walesiak, M. (eds) Data Analysis and Classification. SKAD 2020. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-75190-6_1
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DOI: https://doi.org/10.1007/978-3-030-75190-6_1
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