Multi-relational data mining can be categorized into graph-based and logic-based according to their representation. We talk about the relationship between graph-based data mining and graph-based relational learning. An overview on different methods for graph-based data mining is given. We mainly discuss graph-based relational learning algorithm Subdue,including its advantage and disadvantage. To solves the disadvantages of Subdue,we propose ESubdue,which improve the subgraph isomorphism computation and reduces the times for subgraph isomorphism. Experimental results on both real and synthetic datasets indicate that the improved algorithm is much more efficient than the original one. Finally we conclude the paper and talk about the future work.

The clustering about relational databases is an active study subject in data mining. In this paper, we introduce a Hierarchical Clustering Algorithm Based on Improved Cluster Cohesion and Separation. First, this algorithm joins every table through the tuple ID propagation. Then, groups objects into a large number of relatively small sub-clusters using the shared nearest neighbor algorithm and the improved cluster cohesion algorithm. Last, find the genuine clusters by repeatedly combining these sub-clusters using the improved cluster separation algorithm. The experiment shows the efficiency and scalability of this approach

A multi-relational association rule mining algorithm with guidance of the user is proposed in this paper. A tuple ID propagation approach is used to solve the association rule mining with multiple database relations directly, and introduces the concept of the user's guidance in this paper. The approach has improved user's satisfaction with the mining result. Compared with the traditional algorithms, it improves the accuracy and supports multi-relational databases directly, so its running time is less than that of the multi-relational association rule mining methods based on ILP.

We present a general approach to speeding up a family of multi-relational data mining algorithms that using ID propagation to obtain the information needed for building decision tree classifier from relational database. Preliminary results of our experiments suggest that the proposed method can yield 1-2 orders of magnitude reductions in the running time of such algorithms without any deterioration in the accuracy of results. The proposed modifications enhance the applicability of decision tree algorithms to significantly relational databases that would otherwise be not feasible in practice.