We present a new threshold-based clustering algorithm for news articles. The algorithm consists of two phases: in the first, a local optimum of a score function that captures the quality of a clustering is found with an Expectation-Maximization approach. In the second phase, the algorithm reduces the number of clusters and, in particular, is able to build non-spherical-shaped clusters. We also give a mini-batch version which allows an efficient dynamic processing of data points as they arrive in groups. Our experiments on the TDT5 benchmark collection show the superiority of both versions of this algorithm compared to other state-of-the-art alternatives. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Gallé, M., & Renders, J. M. (2012). Full and mini-batch clustering of news articles with star-EM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7224 LNCS, pp. 494–498). https://doi.org/10.1007/978-3-642-28997-2_49
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