Proposed efficient approach for classification for multi-relational data mining using Bayesian Belief Network

Abstract

Multi-Relational Data Mining is an active area of research for researchers from last many decades. Relational database is an important source of structure data, hence richest source of knowledge. Most of the commercial and application oriented data uses relational database system in which multiple relations are link through primary key, foreign key relationship. Thus, the field of Multi-Relational Data Mining (MRDM) deals with extraction of information from relational database containing multiple tables related with each other. In order to extract important information or knowledge, it is required to apply Data Mining algorithms on this relational database but most of these algorithms works only on single table. Generating a single table may result in to loss of important information, like relation between tuples. Also it is a not efficient in terms of time and space. In this research, we propose a Probabilistic Graphical Model, namely Bayesian Belief Network (BBN), based approach that considers not only attributes of table but also the relation between tables. The conditional dependencies between tables is derived from Semantic Relationship Graph (SRG) of the relational database. This research also aims, to find relevant attributes from Multi-Relational dataset in order to improve the accuracy.