A Comparative Analysis of Classification Techniques on Categorical Data in Data Mining

Main Article Content

Sakshi, Prof. Sunil Khare

Abstract

In recent years, huge amount of data is stored in database which is increasing at a tremendous speed. This requires need for some new techniques and tools to intelligently analyze large data sets to acquire useful information. This growing need demands for a new research field called Knowledge Discovery in Databases (KDD) or Data Mining. The main objective of the data mining process is to extract information from a large data set and transform it into some meaningful form for further use. Classification is the one of data mining techniques which is used to classify categorical data item in a set of data into one of predefined set of classes or groups, In this paper, the goal is to provide a comprehensive analysis of different classification techniques in data mining that includes decision tree, Bayesian networks, k-nearest neighbor classifier & artificial neural network.
DOI: 10.17762/ijritcc2321-8169.150818

Article Details

How to Cite
, S. P. S. K. “A Comparative Analysis of Classification Techniques on Categorical Data in Data Mining”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 3, no. 8, Aug. 2015, pp. 5142-7, doi:10.17762/ijritcc.v3i8.4806.
Section
Articles