Clustering Principles with K-Means

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 metho