Classification for Multi-Relational Data Mining using Bayesian Belief Network

Abstract

Multi-Relational Data Mining is an active area of research from last decade. Relational database is an important source of structured data, hence richest source of knowledge. Most of the commercial and application oriented data uses a relational database scheme in which multiple relations are linked through primary key, foreign key relationship. Multi-Relational Data Mining (MRDM) deals with extraction of information from a relational database containing multiple tables related to 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 work only on a single table. Generating a single table from multiple tables may result in loss of important information, like the relation between tuples, also it is a not efficient in terms of time and space. In this paper, we proposed a Probabilistic Graphical Model, Bayesian Belief Network (BBN), based approach that considers not only attributes of the table but also the relation between tables. The conditional dependencies between tables are derived from Semantic Relationship Graph (SRG) of the relational database, whereas Tuple Id propagation helps to derive the conditional probability of tables. Our model not only predicts class label of unknown samples, but also gives the value of sample if class label is known. © Springer International Publishing Switzerland 2014.