k-means clustering and its usecases
Introduction to K-means Clustering
k-means is one of the simplest unsupervised learning algorithms that solve the clustering problems. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). The main idea is to define k centers, one for each cluster.
K-Means is applied in
The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data and to find patterns and make better decisions.. Once the algorithm has been run and the groups are defined, any new data can be easily assigned to the most relevant group.k-means is one of the simplest unsupervised learning algorithms that solve the clustering problems. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). The main idea is to define k centers, one for each cluster.
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Algorithm
To start with k-means algorithm, you first have to randomly initialize points called the cluster centroids (K). K-means is an iterative algorithm and it does two steps: 1. Cluster assignment 2. Move centroid step.
1. Cluster assignment
the algorithm goes through each of the data points and depending on which cluster is closer, It assigns the data points to one of the three cluster centroids.
2. Move centroid
Here, K-means moves the centroids to the average of the points in a cluster. In other words, the algorithm calculates the average of all the points in a cluster and moves the centroid to that average location.
This process is repeated until there is no change in the clusters (or possibly until some other stopping condition is met). K is chosen randomly or by giving specific initial starting points by the user.