16 research outputs found

    Convex clustering of the European populations from the POPRES data using <i>Ï•</i> = 10 and <i>k</i> = 40.

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    <p>Convex clustering of the European populations from the POPRES data using <i>Ï•</i> = 10 and <i>k</i> = 40.</p

    Convex clustering concepts.

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    <p>For clarity, we present three random data points extracted from the three classes in the Iris dataset. Black points denote the original data points <b><i>X</i></b> and blue points denote the cluster centers <b><i>U</i></b>. At <i>μ</i> = 0, <b><i>X</i></b> and <b><i>U</i></b> coincide. At intermediate <i>μ</i> values (middle figure), <b><i>U</i></b> coalesces towards its cluster center. For sufficiently large <i>μ</i>, <b><i>U</i></b> converges to cluster centers (right figure). Note that in this demonstration, only the left two points have non-zero pairwise weights <i>w</i><sub><i>ij</i></sub>. Hence, the two resulting clusters reflect the two graphs defined by the matrix of weights.</p

    Convex clustering of the breast cancer samples.

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    <p>Points on the plot indicate data vectors projected onto the first and third principal components (PCs) of the sample. Lines trace the cluster centers as they traverse the regularization path.</p

    Magnified view of the convex clustering results for the HGDP data in Europe and Central Asia.

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    <p>Magnified view of the convex clustering results for the HGDP data in Europe and Central Asia.</p

    Hierarchical clustering projection showing genetic relationships among populations in and near the British Isles.

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    <p>Hierarchical clustering projection showing genetic relationships among populations in and near the British Isles.</p
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