K-Means Clustering
K-Means clustering is a process of grouping similar data points into clusters. The algorithm accomplishes this by repeatedly assigning data points to the nearest cluster centroid, re-evaluating the centroids, and achieving convergence to a stable solution. The letter “K” refers to the number of clusters we want to form. The aim of K-Means is to minimize the sum of squared distances between data points and their respective cluster centroids.
The K-Means clustering in Python is one of the partitioning approaches and each cluster will be represented with a calculated centroid. All the data points in the group will have a minimum distance from the computed centroid.
K- means clustering with SciPy
K-meansPrerequisite: K-means clustering
K-means clustering in Python is one of the most widely used unsupervised machine-learning techniques for data segmentation and pattern discovery. This article will explore K-means clustering in Python using the powerful SciPy library. With a step-by-step approach, we will cover the fundamentals, implementation, and interpretation of K-Means clustering, providing you with a comprehensive understanding of this essential data analysis technique.
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