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Performance Optimization of K-Means Clustering using Multiple K-Values: A Hands-On
Subject area: Science,Engineering and Technology · Area of research: Data Science, Machine Learning
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.
How to cite this paper
@article{1706113,
author = {Shivaraj BG, Amrutha, Shwetha Kamath},
title = {Performance Optimization of K-Means Clustering using Multiple K-Values: A Hands-On},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {2},
pages = {25-28},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706113.pdf},
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.},
month = {August},
}