Scala and Spark for Big Data Analytics

Centroid-based clustering

In centroid-based clustering technique, clusters are represented by a central vector. However, the vector itself may not necessarily be a member of the data points. In this type of learning, a number of the probable clusters must be provided prior to training the model. K-means is a very famous example of this learning type, where, if you set the number of clusters to a fixed integer to say K, the K-means algorithm provides a formal definition as an optimization problem, which is a separate problem to be resolved to find the K cluster centers and assign the data objects the nearest cluster center. In short, this is an optimization problem where the objective is to minimize the squared distances from the clusters. ...