Abstract
Driven by the advances in data collection and storage, increasingly large and high dimensional datasets are being stored. Multiple enterprises store large amounts of data for analysis, making the field of data analysis more and important. But how is someone able to gain insights from this massively evolving data? Without special tools, human analysts can no longer make sense of such massive volumes of data. As a consequence, intelligent data mining techniques are being developed either to semi-automate or totally automate the process of data mining. Clustering is an essential step in this process, as it reveals natural structures and identifies interesting patterns in the underlying data, by grouping high dimensional data together. In order to process this data, there have been proposed several clustering algorithms, all of them trying to solve the demanding aspects of todays’ data