A Self-Adaptive Fuzzy<i> c</i>-Means Algorithm for Determining the Optimal Number of Clusters

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left">Dataset </td><td class="align_center">Iris</td><td class="align_center">Wine</td><td class="align_center">Seeds</td><td class="align_center">SubKDD</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">Clustering accuracy</td><td class="align_center">84.00%</td><td class="align_center">96.63%</td><td class="align_center">91.90%</td><td class="align_center">94.35%</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Computational Intelligence and Neuroscience

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Table 4

Table 4: A Self-Adaptive Fuzzy<i> c</i>-Means Algorithm for Determining the Optimal Number of Clusters 

Computational Intelligence and Neuroscience

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

Table 4