Abstract
Optimizing the performance of K-means clustering involves several techniques and strategies that can help speed up the computation and improve the clustering quality. Distortion and inertia are key metrics used to evaluate the quality, assess the clustering performance, and determine the optimal number of clusters of K-means clustering. Using the Elbow Method, we can plot Distortion and Inertia metrics against different values of ???? to determine the optimal number of clusters. This approach helps achieve better clustering results by ensuring that data points are grouped most meaningfully.
Keywords
K-Means Clustering, Distortion, Inertia, Elbow Method.
Citations
IRE Journals:
Shivaraj BG , Amrutha , Shwetha Kamath
"Performance Optimization of K-Means Clustering using Multiple K-Values: A Hands-On" Iconic Research And Engineering Journals Volume 8 Issue 2 2024 Page 25-28
IEEE:
Shivaraj BG , Amrutha , Shwetha Kamath
"Performance Optimization of K-Means Clustering using Multiple K-Values: A Hands-On" Iconic Research And Engineering Journals, 8(2)
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