An Improved Integrated Clustering Learning Strategy Based on Three-Stage Affinity Propagation Algorithm with Density Peak Optimization Theory

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="1"><hr/></td></tr><tr><td class="align_left">Step 1: randomly select <i>k</i> objects as the initial clustering center among <i>n</i> data objects</td></tr><tr><td class="align_left">Step 2: according to the principle of minimum distance, the distance from each data object to the clustering center is calculated and assigned to the nearest cluster</td></tr><tr><td class="align_left">Step 3: the average value of each cluster is recalculated, and the convergence function is calculated until the center of each cluster no longer changes</td></tr><tr><td class="align_left">Step 4: when the cluster center does not change, the algorithm is over; otherwise, it will turn to Step 2</td></tr><tr class="table-tr"><td colspan="1"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The process of the k-means algorithm.</div>

Complexity

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Table 1

Table 1: An Improved Integrated Clustering Learning Strategy Based on Three-Stage Affinity Propagation Algorithm with Density Peak Optimization Theory 

Table 1 | An Improved Integrated Clustering Learning Strategy Based on Three-Stage Affinity Propagation Algorithm with Density Peak Optimization Theory 