An improved frequency based agglomerative clustering algorithm for detecting distinct clusters on two dimensional dataset

In this study, a frequency based Dynamic Automatic Agglomerative Clustering (DAAC) is developed and presented. The DAAC scheme aims to automatically identify the appropriate number of divergent clusters over the two dimensional dataset based on count of distinct representative objects with higher intra thickness and lesser intra separation. The Distinct Representative Object Count (DROC) is introduced to automatically trace the count of distinct representative objects based on frequency of object occurrences. It also identifies the distinct number of highly comparative clusters based on the count of distinct representative objects through sequence of merging process. Experimental result shows that the DAAC is suitable for instinctively identifying the K distinct clusters over the different two dimensional datasets with higher intra thickness and lesser intra separation than existing techniques.   Key words: Dynamic automatic agglomerative clustering, clusters, intra thickness, intra separation, distinct representative object count.