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    PG-means: learning the number of clusters in data.

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    Date
    2007-03-19
    Author
    Feng, Yu.
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    Abstract
    We present a novel algorithm called PG-means in this thesis. This algorithm is able to determine the number of clusters in a classical Gaussian mixture model automatically. PG-means uses efficient statistical hypothesis tests on one-dimensional projections of the data and model to determine if the examples are well represented by the model. In so doing, we apply a statistical test to the entire model at once, not just on a per-cluster basis. We show that this method works well in difficult cases such as overlapping clusters, eccentric clusters and high dimensional clusters. PG-means also works well on non-Gaussian clusters and many true clusters. Further, the new approach provides a much more stable estimate of the number of clusters than current methods.
    URI
    http://hdl.handle.net/2104/5021
    Department
    Computer Science.
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    • Electronic Theses and Dissertations
    • Theses/Dissertations - Computer Science

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    Copyright © Baylor® University All rights reserved. Legal Disclosures.
    Baylor University Waco, Texas 76798 1-800-BAYLOR-U
    Baylor University Libraries | One Bear Place #97148 | Waco, TX 76798-7148 | 254.710.2112 | Contact: libraryquestions@baylor.edu
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    DSpace software copyright © 2002-2016  DuraSpace
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