Fast hierarchical clustering based on compressed data

Clustering in data mining is the process of discovering groups in a dataset, in such a way, that the similarity between the elements of the same cluster is maximum and between different clusters is minimal. Some algorithms attempt to group a representative sample of the whole dataset and later to perform a labeling process in order to group the rest of the original database. Other algorithms perform a pre-clustering phase and later apply some classic clustering algorithm in order to create the final clusters. We present a pre-clustering algorithm that not only provides good results and efficient optimization of main memory but it also is independent of the data input order. The efficiency of the proposed algorithm and a comparison of it with the pre-clustering BIRCH algorithm are shown.