Fast mining of association rules in large-scale problems
We study the scalability problem of a priori-like algorithms that are used in mining association rules. We show how a priori suffers from performance deterioration for large-scale problems and propose alternative data structures and operations that can be used to apply the a priori-trick optimization method in large-scale problems. In the proposed method, the database is transformed into a more efficient structure that is used along with the intersection operation, to find the frequent itemsets in the database. The performance evaluation shows that, with a minor increase in the storage requirement, the proposed technique outperforms significantly the existing algorithms especially in large-scale problems.
Many data analysis problems involve an investigation of relationships between attributes in heterogeneous databases, where different prediction models can be more appropriate for different regions. We propose a technique of integrating global and local random subspace ensemble. We performed a comparison with other well known combining methods on standard benchmark datasets and the proposed technique gave better accuracy.