Optimal initialization of K-means using Particle Swarm Optimization

04/19/2019
by   Ashutosh Mahesh Pednekar, et al.
0

This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data could act as a fitness measure. The resultant optimal vector can be used as the initial centroids for K-means.

READ FULL TEXT
research
09/06/2018

A tutorial on Particle Swarm Optimization Clustering

This paper proposes a tutorial on the Data Clustering technique using th...
research
04/18/2020

Color Image Segmentation using Adaptive Particle Swarm Optimization and Fuzzy C-means

Segmentation partitions an image into different regions containing pixel...
research
05/24/2005

A dissipative particle swarm optimization

A dissipative particle swarm optimization is developed according to the ...
research
07/03/2017

Modeling preference time in middle distance triathlons

Modeling preference time in triathlons means predicting the intermediate...
research
05/01/2019

Recombinator-k-means: Enhancing k-means++ by seeding from pools of previous runs

We present a heuristic algorithm, called recombinator-k-means, that can ...
research
06/12/2019

A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble

This paper presents a k-means-based multi-subpopulation particle swarm o...
research
07/31/2020

Anakatabatic Inertia: Particle-wise Adaptive Inertia for PSO

Throughout the course of the development of Particle Swarm Optimization,...

Please sign up or login with your details

Forgot password? Click here to reset