Iceberg query (IBQ) is a special class of aggregation query which compute aggregations upon user provided threshold (T). In data mining area, efficient evaluation of iceberg queries has been attracted by many researchers due to enormous production of data in industries and commercial sectors. Decision support database and discovery of knowledge related systems mainly compute aggregate values of interesting attributes by handling a big quantity of data in large databases. In literature, different strategies were found for IBQ evaluation, but using compressed bitmap index technique provides efficient strategy among all. In this paper, we propose a new strategy for computing IBQ, which builds a set for each attribute value, contains its occurrences in the attribute column and performs set operations for producing result. An experimentation on synthetic dataset demonstrates our approach is efficient than existing strategies for lower thresholds. We suggested set operations[11] in place of bitwise-AND operations to reduce execution time for different threshold values. And we developed effective GUI for aggregation of Different item pairs

Database iceberg query threshold set operations