ABSTRACT
Classification could be broadly divided into unsupervised learning (also known as clustering) and supervised learning. When a set of data or observations is provided with the objective of establishing clusters or classes in the data, it is termed unsupervised learning. Whereas when we know that there are certain numbers of groups and the objective is to build a rule to classify new data into one of the existing groups, it is termed supervised learning. The various characteristics expected in a classifier are accuracy, speed of classification, comprehensibility, and time to learn (Henery, 1994).
