Design and Analysis of an Efficient Multi-Relational Decision Tree Learning Algorithm

Abstract

Abstract In today’s day of modern era when the data handling objectives are getting bigger and bigger with respect to volume, learning and inferring knowledge from complex data becomes the utmost problem. The research in Knowledge Discovery in Databases has been primarily directed to attribute-value learning in which one is described through a fixed set tuple given with their values. Database or dataset is seen in the form of table relation in which every row corresponds to an instance and column represents an attribute respectively. In this paper a New framework is introduced a much more sophisticated and deserving approach i.e., Hybrid Multi-Relational Decision Tree Learning Algorithm which overcomes with Exiting technology drawbacks and other anomalies. Result show that Hybrid Multi- Relational Decision Tree Learning Algorithm provides certain methods which reduces its execution time. Experimental results on different datasets provide a clear indication that Hybrid Multi-Relational Decision Tree Learning Algorithm is comprehensively a better approach.