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Solving k-means on High-dimensional Big Data

Dozenten: Jan-Philipp W. Kappmeier, Daniel R. Schmidt, Melanie Schmidt


In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement. For the k-means problem, this has led to the development of several (1+eps)-approximations (under the assumption that k is a constant), but also to the design of algorithms that are extremely fast in practice and compute solutions of high accuracy. However, when not only the length of the stream is high but also the dimensionality of the input points, then current methods reach their limits.

We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high dimensional data in one pass and output a solution of high quality. While piecy is suited for high dimensional data with a medium number of points, piecy-mr is meant for high dimensional data that comes in a very long stream.
We provide an extensive experimental study to evaluate piecy and piecy-mr that shows the strength of the new algorithms.


full version of our paper at arxiv.org