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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.
References
[1] Abiodun M. Ikotun,Absalom E. Ezugwu, Laith Abualigah, Belal Abuhaija, Jia Heming: K- means Clustering Algorithms: A Comprehensive Review, Variants Analysis, and Advances in the Era of Big Data".
[2] Mohiuddin Ahmed, Raihan Seraj, Syed Mohammed Shamsul Islam: "The K-means Algorithm: A Comprehensive Survey and Performance Evaluation".
[3] Ravil Mussabayev, Rustam Mussabayev: "Comparative Analysis of Optimization Strategies for K-means Clustering in Big Data Contexts: A Review".
[4] Abiodun M. Ikotun, Absalom E. Ezugwu:"Boosting K-means Clustering with Symbiotic Organisms Search for Better Performance.
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},
}