K-means clustering algorithm with solve example: how it works | NerdML
This video will help you to understand how we can make use of K-Means Clustering algorithm for solving unsupervised learning problem. We will mathematically solve the problem. We will understand what is K-Means Cluster, what is Euclidean Distance & centroid and how can we apply all these in Clustering algorithm.
The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively:
(1) for each instance, we assign it to a cluster with the nearest centroid, and
(2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
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Prerequisites
Basic understanding of Linear Algebra, Probability, Calculus, Matrix & Python programming including pandas, numpy, scikit learn & some visualization tools.
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Creator : Rahul Saini
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