For data analysis, the partitioning into groups based on data characteristics is crucial. This process is called clustering and the result is a set of groups containing original data, where data in the same group are more similar to each other than to data in other groups. The clustering process only partitions objects into clusters, so at the end of the process the number of object is the same as the original, with the additional information about their division in groups. Since in real world the data sets are likely to contain a huge amount of data, in this work a way to reduce this amount maintaining most important features of data is presented.