Investigation of data clustering preprocessing algorithm on independent attributes to improve the performance of CLONALG

It is a popularly held belief that preprocessing of data generally improves the classification efficiency of data mining algorithms. We study the effects of preprocess by utilizing an algorithm to cluster points in a data set based upon each attribute independently, resulting in additional information about the data points with respect to each of its dimensions. Noise, data boundaries are identified and the cleaned data subset is used to study the performance of CLONALG data mining algorithm against unprocessed dataset.