K – Means Clustering Algorithm - StepUp Analytics Machine learning

"What gets measured, gets managed " – Peter Drucker The most important aim of all the clustering techniques is to group together the similar data points. K-means clustering algorithm is an unsupervised machine learning algorithm. It is a method of vector quantization that aims at grouping the similar data by minimizing the squared error function. You can apply k-means to any clustering problem provided you are having proper feature vector (Vector-Space Model) from data points and a similarity/distance measure that can measure similarity/distance between the feature vectors. The k-means clustering algorithm is used when you have unlabeled data (i.e., data without defined categories or groups).