Clustering principles with kmeans| International Journal of Innovative Science and Research Technology

Clustering Principles with K-Means

Authors : Hena Chauhan; Mehul Pravinchandra Barot

Volume/Issue : Volume 8 - 2023, Issue 2 - February

Google Scholar : https://bit.ly/3IIfn9N

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

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