From the course: Machine Learning with Python: k-Means Clustering
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What is k-means clustering? - Python Tutorial
From the course: Machine Learning with Python: k-Means Clustering
What is k-means clustering?
- [Instructor] One of the most commonly used clustering techniques is known as K-Means clustering. K-Means clustering is a partitional clustering approach. This means that the cluster boundaries are independent of each other. Each item can only belong to one cluster and every item is assigned to a cluster. With K-Means clustering, we start by specifying how many clusters, K, we want. Then the algorithm uses a process known as expectation maximization to assign every item within the dataset to one and only one of K non overlapping clusters based on similarity. To illustrate how K-Means clustering works, let's imagine that we have a data set as represented here with 12 instances and two features, A and B. If our goal is to partition this data into three separate clusters, we set the value of K to three and let the K-Means clustering algorithm do the rest. The first thing that the algorithm does is choose K random points…
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