Visual Clustering Results of Algorithms on Smile Dataset.

Visual Clustering Results of Algorithms on Smile Dataset.

<p><b>(a)</b> DPC-DG, <b>(b)</b> K-means, <b>(c)</b> HDBSCAN, <b>(d)</b> DPC, <b>(e)</b> DPCSA, <b>(f)</b> FCM.</p>

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Bibliographic Details
Main Author:	Wei Xingqiong (21492703) (author)
Other Authors:	Li Kang (494757) (author)
Published:	2025
Subjects:	Cell Biology Molecular Biology Biotechnology Evolutionary Biology Infectious Diseases Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> clustering world datasets demonstrate optimizing clustering indices enabling clustering without algorithm &# 8217 density peak clustering algorithm automatically adjusts cluster centers must requires manual tuning calculate local density cluster centers local density algorithm dependent manual input automatically determining widely used various fields several limitations pattern recognition manually specified image segmentation human intervention fundamental tool experimental results delaunay graph data science data points data mining cutoff distance approach uses
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Summary:	<p><b>(a)</b> DPC-DG, <b>(b)</b> K-means, <b>(c)</b> HDBSCAN, <b>(d)</b> DPC, <b>(e)</b> DPCSA, <b>(f)</b> FCM.</p>

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