Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques

Abstract

Classification is a data mining technique used to predict group membership for data instances within a given dataset. It is used for classifying data into different classes by considering some constrains. The problem of data classification has many applications in various fields of data mining. This is because the problem aims at learning the relationship between a set of feature variables and a target variable of interest. Classification is considered as an example of supervised learning as training data associated with class labels is given as input. Classification algorithms have a wide range of applications like Customer Target Marketing, Medical Disease Diagnosis, Social Network Analysis, Credit Card Rating, Artificial Intelligence, and Document Categorization etc. Several major kinds of classification techniques are K-Nearest Neighbor classifier, Naive Bayes, and Decision Trees. This paper focuses on study of various classification techniques, their advantages and disadvantages.