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- W2554676448 abstract "We introduce an efficient distributed implementation of nearest neighbour mean shift clustering (NNMS). The computationally intensive nature of NNMS has so far restricted its application to complex data sets where a flexible clustering with non-ellipsoidal clusters would be beneficial. A parallel implementation of the standard serial NNMS algorithm on its own brings insufficient performance gains so we introduce two further algorithmic improvements: a normal scale (NS) choice of the optimal number of nearest neighbours, and locality sensitive hashing (LSH) to approximate nearest neighbour searches. Combining these improvements into a single distributed algorithm DNNMS offers the potential for an efficient method for Big Data Clustering." @default.
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- W2554676448 date "2016-07-01" @default.
- W2554676448 modified "2023-09-25" @default.
- W2554676448 title "Distributed mean shift clustering with approximate nearest neighbours" @default.
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- W2554676448 doi "https://doi.org/10.1109/ijcnn.2016.7727595" @default.
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