A Multi-Relational Decision Tree Learning (MRDTL) Approach: A Survey

Abstract

Data Mining is the process of extracting useful knowledge from large set of data. There are number of data mining techniques available to find hidden knowledge from huge set of Data. Among these techniques classification is one of the techniques to predict the class label for unknown data based on previously known class labeled dataset. Several classification techniques like decision tree induction, Naivy Bayes model, rough set approach, fuzzy set theory and neural network are used for pattern extraction. Now a day's most of the real world data stored in relational database but the decision tree induction method is used to find knowledge from flat data relations only, but can't discover pattern from relational database. So to extract multi-relational pattern from relational tables we use MRDTL approach. In real world Missing value problem are common in many data mining application. This paper provides survey of multi-relational decision tree learning algorithm to discover hidden multi-relational pattern from relational data sets and also includes some simple technique to deal with missing value. Keyword: Data Mining, Multi-relational Data Mining Framework, Multi-relational Decision Tree Learning (MRDTL), Relational Database, Missing Value