A novel approach to improve the performance of divisive clustering-BST

Abstract

The traditional way of searching data has many disadvantages. In this context we propose Divisive hierarchical clustering method for quantitative measures of similarity among objects that could keep not only the structure of categorical attributes but also relative distance of numeric values. For numeric clustering, the quantity of clusters can be approved through geometry shapes or density distributions, in the proposed Divclues-T Calculate the Arithmetic mean it is called as a root node, the objects smaller than root node fall into left sub tree otherwise right sub tree this process is repeated until we find singleton object.