Parametrized MTree Clusterer for Weka

In the area of clustering, proposing or improving new algorithms represents a challenging task due to an already existing well-established list and various implementations that allow rapid evaluation against tasks on publicly available datasets. this work, we present improved version MTree clustering algorithm has been implemented within Weka workbench. The algorithmic approach starts from classical metric spaces integrates parametrized business logic for finding optimal number clusters, choosing division policy other characteristics. result is versatile data structure may be used in context but mainly loading datasets, which have known structure. Experimental results show manages find right two tasks, although fail ways. A discussion topics related further improvements experiments real datasets included.