Fast Density Clustering Algorithm for Numerical Data and Categorical Data

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static-preview.hindawi.com/articles/mpe/volume-2017/6393652/figures/6393652.fig.009a.svgz" name="6393652.fig.009a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Cluster center self-determination process based on FDCA</td></tr><tr class="fig-image" id="b"><td><object data="https://static-preview.hindawi.com/articles/mpe/volume-2017/6393652/figures/6393652.fig.009b.svgz" name="6393652.fig.009b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>The same color numbers belong to the same cluster, while those grey images do not belong to any cluster. For each cluster, those images with tiny black circle are cluster centers for each cluster</td></tr></table>

<div>MNIST recognition results based on FDCA.</div>

Mathematical Problems in Engineering

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Figure 9

Figure 9: Fast Density Clustering Algorithm for Numerical Data and Categorical Data 