- Volume 7 Issue 5
In this paper, we propose DBSCAN-I that is an algorithm for clustering with influence. DBSCAN-I that extends traditional DBSCAN and DBSCAN-W converts from non-spatial feature to influence while doing spatial clustering. This is an algorithm that increases probability of allocation to cluster when influence is more higher than other. And also, we present the result that selects effectively optimum allocation with influence to apply the proposed algorithm.