Authors :
Hena Chauhan; Mehul Pravinchandra Barot
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3YKzigS
DOI :
https://doi.org/10.5281/zenodo.7655874
Abstract :
K-means clustering is a method of
unsupervised learning that is used to partition a dataset
into a specific number of clusters (k) to identify patterns
and underlying structures within the data. It is
particularly useful for identifying patterns and structures
in large datasets and is often used as a preprocessing step
for other machine learning algorithms. It has been used
in a wide variety of fields, including data mining, machine
learning, pattern recognition, and image processing. In
this paper, we will discuss some of the advantages and
disadvantages of using the method
Keywords :
Clustering, algorithm, data, clusters, dataset, method
K-means clustering is a method of
unsupervised learning that is used to partition a dataset
into a specific number of clusters (k) to identify patterns
and underlying structures within the data. It is
particularly useful for identifying patterns and structures
in large datasets and is often used as a preprocessing step
for other machine learning algorithms. It has been used
in a wide variety of fields, including data mining, machine
learning, pattern recognition, and image processing. In
this paper, we will discuss some of the advantages and
disadvantages of using the method
Keywords :
Clustering, algorithm, data, clusters, dataset, method