Hands-On Guide To ROCK Clustering Algorithm

Clustering is a part of unsupervised subject learning where the major task is to make groups of data points such that the grouped data points have similarities. The basic clustering algorithm procedure consists of a basic concept to determine the distance between the data points and divide them into groups. The closest points are being separated into one group. There are various algorithms available that work on only these basic phenomena or by improving the same phenomena. And most of the algorithms are very good at dealing with the data where they don't have categorical values available on the data or the data is fully scattered in the space. We can't specify that the data point belongs to which category. Here if a data has categorical values, the clustering for categorical or boolean at