Data aggregation for capacity management

This thesis presents a methodology for data aggregation for capacity management. It is assumed that there are a very large number of products manufactured in a company and that every product is stored in the database with its standard unit per hour and attributes that uniquely specify each product. The methodology aggregates products into families based on the standard units-per-hour and finds a subset of attributes that unambiguously identifies each family. Data reduction and classification are achieved using well-known multivariate statistical techniques such as cluster analysis, variable selection and discriminant analysis. The experimental results suggest that the efficacy of the proposed methodology is good in terms of data reduction.