Feb 15, 2015 · We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high ...
Jun 1, 2015 · Solving k-means on High-dimensional Big Data. Jan-Philipp W ... Our aim is to compute coresets for large high-dimensional data sets by using BICO ...
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Abstract. In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little.
Jan 28, 2021 · KMeans is one of the most popular clustering algorithms, and sci-kit learn has made it easy to implement without us going too much into mathematical details.
Missing: Solving | Show results with:Solving
Jun 17, 2019 · Yes, k-means struggles in high dimensions because of the geometry behind high-dimensional spheres. The likelihood of clusters forming in nice, ...
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Aug 30, 2016 · Under the assumption that 10 dimensions is 'too high' for k-means, the simplest strategy would be to count the number of features you have. But ...
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Oct 11, 2019 · To find the optimal k - we run multiple Kmeans in parallel and pick the one with the best silhouette score. In 90% of the cases we end up with k ...
Nov 23, 2015 · K-means cannot be used on such data. k-means only works on low-dimensional, continuous numeric, dense data. So if you have say 10 dimensions (at ...
Jun 20, 2015 · We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high ...
May 11, 2015 · To simplify, in higher dimensions, the distance metric used in K-means becomes meaningless. Therefore, either change your distance metric with ...