These days, there is no shortage of clustering approaches available, all of which are employed exclusively for the purpose of spatial data mining. A few of them use K-means, Clarans, DBscan, and numerous more techniques. Similar to this, there is a distributed dynamic clustering technique that uses local locations for clustering and then aggregates the local clusters that are obtained at these sites. Every local site produces local clusters, which are then sent to the global site for aggregation. In the aggregation step, local clusters that have been obtained from local sites are combined and connected to create global clusters. The current techniques cause great confusion in the aggregate step. The contour algorithm is used to determine the borders of the local clusters that are obtained during the parallel phase, which is followed by the aggregation phase. Global clusters are framed by the accumulation of local clusters. That being said, the aggregation process is incredibly intricate. This work modifies the technique to separate the aggregation and also does a comparison with conventional clustering algorithms.