Data mining: Classification by decision tree induction
C. K. Praseeda, B. L. Shivakumar · Asian Journal of Research in Social Sciences and Humanities · 2017
Data mining is a promising and flourishing frontier in database systems and new database applications. Data mining, referred to as knowledge discovery in databases, is the automated or appropriate extraction of patterns representing knowledge implicitly stored in large databases, data warehouses and other massive information repositories. Classification and prediction are two forms of data analysis that can be used to extract models describing important data classes or to predict future trends. Whereas classification predicts categorical labels, prediction models continuous valued functions. To improve the accuracy, efficiency and scalability of the classification process, different pre-processing steps can be applied. Many classification methods have been proposed by researchers in machine learning, expert systems statistics and neurobiology. This paper describes basic theory of automatically extracting models from data using decision tree induction method. Decision tree algorithm is explained both at a conceptual level and with a fair amount of technical details. This algorithm deals robustly and sensibly with real-world problems such as numeric attributes, missing values and noisy data.