IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 2, Ver. IX (Mar-Apr. 2014), PP 74-81 www.iosrjournals.org www.iosrjournals.org 74 | Page A Multi-Relational Decision Tree Learning (MRDTL) Approach: A Survey Patel Rinkal 1 , Rajanikanth Aluvalu 2 M.Tech Student Computer Engineering Department, R.K University Rajkot 1 Computer Engineering Department, R.K University Rajkot 2 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 I. Introduction In today’s world structured data is stored in relational databases. Many important classification approaches, such as neural networks and SVM technique, can only be applied to data represented by single flat data relations. And it is very difficult to convert a multi-relational database into a single flat relation without losing important information. The developments of high throughput data achievement, digital storage, and communications technologies have made it possible to collect very large amounts of data in many scientific and commercial domains. Most of this data are stored in multiple relations. So, the task of learning from relational database has begun to receive important attention [1]. A. What is Data Mining? Data mining is the process of extracting and finding patterns from huge data sets by combining methods from statistics and artificial intelligence. Data mining consist of different techniques like classification, clustering, prediction, outlier analysis etc. for finding hidden knowledge. Classification [16] predicts categorical class label and construct model based on training set data which contain class label. This model represented as classification rule. Classification [12] is supervised learning. Data mining [12] has a variety of fields which provides the different tools and the techniques for handling the large database. Through this technique we will obtain the new, valuable non-trivial and existing information. KDD and Data Mining sometimes are used imprecisely. A more recent convention mentioned in [18] (Blockeel, 1998) establishes that the process of knowledge discovery actually contains of three subtasks. The first task is to adjust the original format of the data to fit the input format of the data mining algorithm (called Preprocessing of data). Once the data are formatted, one or more algorithms must be applied to find out patterns, regularities or general laws from the data, this is the phase called Data Mining. Once the results of the data mining process are obtained they may require to be translated to a more understandable format. This last stage is known as post- processing of the results. Data Mining Techniques are,  Classification Technique  Clustering Technique  Association rule mining  Prediction  Outlier Analysis  Characterization and Description[13]