Associate Professor, Gokaraju Rangaraju Institute of Engineering and Technology
Abstract
The traditional way of converting data into a single type has many disadvantages. In this context we propose an agglomerative 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 aquatic data clustering, the number of clusters can be validated through geometry shapes or density distributions, the proposed hierarchical and partitioning methods the relationships among categorical items. In numeric clustering, the number of clusters can be validated through geometry shapes or density distributions, the proposed hierarchical and partitioning methods the relationships among categorical items In This Paper we here investigate linkage critions in hierarchical aquatic data clustering algorithm performance calculations using with Euclidian distance measure and some clustering techniques and their applications have been discussed. It also describes the necessities to be calculated for constructing an well-organized to handle the huge data sets. As the study initially investigates distinct issues for creating clusters with numeric attributes
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