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- W2103452682 abstract "How do we find a natural clustering of a real-world point set which contains an unknown number of clusters with different shapes, and which may be contaminated by noise? As most clustering algorithms were designed with certain assumptions (Gaussianity), they often require the user to give input parameters, and are sensitive to noise. In this article, we propose a robust framework for determining a natural clustering of a given dataset, based on the minimum description length (MDL) principle. The proposed framework, robust information-theoretic clustering (RIC) , is orthogonal to any known clustering algorithm: Given a preliminary clustering, RIC purifies these clusters from noise, and adjusts the clusterings such that it simultaneously determines the most natural amount and shape (subspace) of the clusters. Our RIC method can be combined with any clustering technique ranging from K-means and K-medoids to advanced methods such as spectral clustering. In fact, RIC is even able to purify and improve an initial coarse clustering, even if we start with very simple methods. In an extension, we propose a fully automatic stand-alone clustering method and efficiency improvements. RIC scales well with the dataset size. Extensive experiments on synthetic and real-world datasets validate the proposed RIC framework." @default.
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- W2103452682 date "2007-12-01" @default.
- W2103452682 modified "2023-09-25" @default.
- W2103452682 title "RIC" @default.
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- W2103452682 doi "https://doi.org/10.1145/1297332.1297334" @default.
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