Data Reduction Techniques in Data Mining

Dimension reduction of datasets is very useful in different application including classification, compression, Text documents, microarray gene-expressions, images, etc The challenge in distributed data mining is how to learn as much knowledge from distributed databases as we do from the centralized database without costing too much communication bandwidth. In this paper we propose a data reduction algorithm, which is different from all of these methods, to encode the transactions which reduce the size of transaction that in turn reduces the transfer time, size of the transaction, cost as well as increase the security level of data.