Channel: Learn By Watch

Category: Education

Tags: k means clusteringmachine learning coursecluster analysis machine learningmachine learning k means clusteringunsupervised learningk means clustering visualizationk means clustering examplek means clustering pythonclustering algorithmsk means algorithmmachine learning tutorialk-meansclustering in machine learningk means clustering algorithmclusteringcluster analysis

Description: K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. ... Data points are clustered based on feature similarity. In this video you will learn about these topics: ● Clustering example - Explanation of clustering by taking example of heavenly bodies and understood how changes in definition of a class can move an item from one cluster to the other ● Free hand clustering - Took a dataset and roughly tried to form clusters. Eventually found out that there are many ways in which clusters can be formed as well as the number of clusters are also not fixed. ● Algorithm of k-means clustering - Defined the steps which leads to clustering : ○ Starting with assuming that there will be 2 clusters ○ Set centroids to initial position ○ Make the centroid stabilized by looking at the sum of squared errors(SSE). ○ Increment the number of clusters ● Plot SSE Vs number of clusters - Look at the elbow of this graph which helps in finding out the optimal number of clusters for our given problem. ● Predict output - Predict the output by finding out in which cluster is the point located and assign it to the class.