Diverse Clustering techniques make diverse assumptions on the construction of your data, frequently defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity concerning members of the same cluster, and separation, the distinction between clusters. Other methods are dependant on approximated density and graph connectivity. https://sokkup.com/bbs/board.php?bo_table=free&wr_id=1532