Akhila et al. Comparative Study of Classification Algorithms Using Data

Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Classification is a data mining process that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. Some classification algorithms used in data mining include Apriori, Decision Trees and Naïve Bayes algorithm. In this paper, we apply these classification algorithms to a log data and carry out a comparative analysis to find out which algorithm is best suited for such analysis