A computational environment for extracting rules from databases

Classification for very large databases has many practical applications in Data Mining. Thus, Machine Learning algorithms should be able to operate in massive datasets. When a dataset is too large for a particular learning algorithm to be applied, there are other ways to make learning feasible; preprocessing techniques and dataset sampling can be used to scale up classifiers to large datasets. In this work we propose a computational environment based on two architectures, one for data pre-processing and one for post-processing which allow evaluation of induced knowledge. The two architecture share a set of learning systems, which can be enhanced to support new ones. The environment is designed as a test-bed for Data Mining research, as well as a generic knowledge discovery tool for varied database domains. Flexibility is achieved by an open-ended design for extensibility, enabling integration of existing Machine Learning algorithms, support functions for pre-processing as well as new locally developed algorithm and functions.