Abstract
By high-dimensional we mean dimensionality of the same order as the number of objects or observations to cluster, and the latter in the range of thousands upwards. Bellman’s “curse of dimensionality” applies to many widely-used data analysis methods in high-dimensional spaces. One way to address this problem is by array permuting methods, involving row/column reordering. Such methods are closely related to dimensionality reduction methods such as principal components analysis. An imposed order on an array is beneficial not only for visualization but also for use of a vast range of image processing methods. For example, clustering becomes in this context image feature detection.
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Murtagh, F. (2002). Clustering in High-dimensional Data Spaces. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_9
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DOI: https://doi.org/10.1007/978-3-642-56181-8_9
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