What is clustering explain K means algorithm with an example?

K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid. In this algorithm, the data points are assigned to a cluster in such a manner that the sum of the squared distance between the data points and centroid would be minimum.

What is K means clustering explain it?

K-means clustering aims to partition data into k clusters in a way that data points in the same cluster are similar and data points in the different clusters are farther apart. Similarity of two points is determined by the distance between them. There are many methods to measure the distance.

What is the main difference between KNN and K-Means Clustering?

They are often confused with each other. The ‘K’ in K-Means Clustering has nothing to do with the ‘K’ in KNN algorithm. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification.

What is the fundamental difference between K-Means Clustering and KNN?

K-means clustering represents an unsupervised algorithm, mainly used for clustering, while KNN is a supervised learning algorithm used for classification.

Which method is more robust among K means and K-Medoids clustering justify your answer?

K-Medoids is more robust because less sensitive to outliers. K-Means is more efficient. It takes more time to define distances between each diamond than to compute a mean. There are other ways to do clustering.

Is K means clustering hierarchical?

In K Means clustering, since we start with random choice of clusters, the results produced by running the algorithm multiple times might differ. While results are reproducible in Hierarchical clustering. K Means is found to work well when the shape of the clusters is hyper spherical (like circle in 2D, sphere in 3D).