Result evaluation of transaction and occurrences based on density and zonal minimum support

In this paper we proposes an efficient approach based on apriori algorithm. We use density minimum support so that we reduce the execution time. Our approach supports the zonal minimum support, by this approach we can store the transaction on the daily basis, then we provide three different density zone based on the transaction and minimum support which is low(L), Medium(M), High(H). Based on the zonal support we categorize the item set for pruning. So our approach is useful for pruning the data zone wise, because the support categorization is not same in all the places, it must be categorized by the population visitors. So the main aim is to classify and detection automatically density wise. Our algorithm provides the flexibility for improved association and dynamic support. Comparative result shows the effectiveness of our algorithm.