UH Biocomputation Group - Minkowski metrichttp://biocomputation.herts.ac.uk/2021-03-10T15:01:00+00:00Minkowski Metric, Feature Weighting and Anomalous Cluster Initializing in K-Means Clustering2021-03-10T15:01:00+00:002021-03-10T15:01:00+00:00Deepak Pandaytag:biocomputation.herts.ac.uk,2021-03-10:/2021/03/10/minkowski-metric-feature-weighting-and-anomalous-cluster-initializing-in-k-means-clustering.html<p class="first last">Deepak Panday's Journal Club session where he will talk about a paper "Minkowski Metric, Feature Weighting and Anomalous Cluster Initializing in K-Means Clustering"</p>
<p>This week on Journal Club session Deepak Panday will talk about a paper "Minkowski Metric, Feature Weighting and Anomalous Cluster Initializing in K-Means Clustering".</p>
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<p>This paper represents another step in overcoming a drawback of
K-Means, its lack of defense against noisy features, using feature
weights in the criterion. The Weighted K-Means method by Huang et al.
(2008, 2004, 2005) [5, 7] is extended to the corresponding Minkowski
metric for measuring distances. Under Minkowski metric the feature
weights become intuitively appealing feature rescaling factors in a
conventional K-Means criterion. To see how this can be used in
addressing another issue of K-Means, the initial setting, a method to
initialize K-Means with anomalous clusters is adapted. The Minkowski
metric based method is experimentally validated on datasets from the
UCI Machine Learning Repository and generated sets of Gaussian
clusters, both as they are and with additional uniform random noise
features, and appears to be competitive in comparison with other
K-Means based feature weighting algorithms.</p>
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<p>Papers:</p>
<ul class="simple">
<li>R. Cordeiro de Amorim, B. Mirkin, <a class="reference external" href="https://doi.org/10.1016/j.patcog.2011.08.012">"Minkowski Metric, Feature Weighting and Anomalous Cluster Initializing in K-Means Clustering"</a>, 2012, Pattern Recognition, 45, 1061--1075</li>
</ul>
<p><strong>Date:</strong> 2021/03/10 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>