Saved in:
Bibliographic Details
Main Authors: Rishabh Sharma, Dr. Shipra Arora
Format: Recurso digital
Language:
Published: Zenodo 2022
Subjects:
Online Access:https://doi.org/10.5281/zenodo.18439850
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866901797672583168
author Rishabh Sharma
Dr. Shipra Arora
author_facet Rishabh Sharma
Dr. Shipra Arora
contents As we know, Clustering analysis is a very important feature of machine learning. This process divides the data points on the basis of their characteristics. Similar data points come into the same group and unsimilar data points in different groups. So basically it makes a group of similar data points study further. This paper analysis K-means clustering algorithm by taking an example of cricket stats in which player capability is determined using their stats like wickets taken and run scored in their career. This capability can be used for the team selection purpose by selecting appropriate batsman, allrounders and bowler.
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_18439850
institution Zenodo
language
publishDate 2022
publisher Zenodo
record_format zenodo
spellingShingle Analysis of K-Means Clustering Algorithm
Rishabh Sharma
Dr. Shipra Arora
Analysis
K-means
clustering
pattern matching
As we know, Clustering analysis is a very important feature of machine learning. This process divides the data points on the basis of their characteristics. Similar data points come into the same group and unsimilar data points in different groups. So basically it makes a group of similar data points study further. This paper analysis K-means clustering algorithm by taking an example of cricket stats in which player capability is determined using their stats like wickets taken and run scored in their career. This capability can be used for the team selection purpose by selecting appropriate batsman, allrounders and bowler.
title Analysis of K-Means Clustering Algorithm
topic Analysis
K-means
clustering
pattern matching
url https://doi.org/10.5281/zenodo.18439850