Using Data and Models at Mixed Frequencies in Computation and Forecasting
This important book, prepared under the direction of Nobel Laureate Lawrence R. Klein, shows how economic forecasts are made. It explains how modern developments in information technology have made it possible to forecast frequently – at least monthly but also weekly or bi-weekly – depending upon the perceived needs of potential forecast users and also on the availability of updated material.
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.