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- W1509374987 abstract "This paper discusses how numerical gradient estimation methods may be used in order to reduce the computational demands on a class of multidimensional clustering algorithms. The study is motivated by the recognition that several current point-density based cluster identification algorithms could benefit from a reduction of computational demand if approximate a-priori estimates of the cluster centres present in a given data set could be supplied as starting conditions for these algorithms. In this particular presentation, the algorithm shown to benefit from the technique is the Mean-Tracking (M-T) cluster algorithm, but the results obtained from the gradient estimation approach may also be applied to other clustering algorithms and their related disciplines." @default.
- W1509374987 created "2016-06-24" @default.
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- W1509374987 date "1997-01-01" @default.
- W1509374987 modified "2023-09-23" @default.
- W1509374987 title "On merging gradient estimation with mean-tracking techniques for cluster identification THE CURSE OF DIMENSIONALITY" @default.
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