Clustering is identified as one of the major tasks in the databases that are spatial in nature. The discovery of regions that are of more interest in large cities is an trivial task. In the recent work to solve this issue, new Path Nearby Cluster (PNC) query algorithm is introduced which finds locations of specific interest with respect to a user-specified travel route. However supporting Reverse Path nearby Cluster (RPNC) query has a broad application domains which includes decision support, marketing that are based on certain profiles and resource assignment. Recent work doesn’t efficient support reverse based PNC query system; to solve this issue proposed work is extended to support reverse based PNC query algorithm, known as RPNC. RPNC query algorithm which focuses on the inverse relation among objects. The proposed work first introduces a new spatial query named RPNC is used to find out the appropriate locations that are accessible in the road networks. Given road network dataset and a list of location candidates specified by users, and the RPNC query returns the location with the highest importance constraint. In the RPNC, clusters are formed by using Enhanced Density Clustering (EDC). Then top k -RPNC are reversed and sorted according to their highest density distributions and they are scanned from the maximum to the minimum. These values are found via the use of the Improved Bat Search algorithm (IBSA). The upper and lower bound pairs in the proposed method are then optimized for pruning the space of search in the domains under consideration in a global manner. The performance of the proposed RPNC- IBSA query processing is verified by extensive experiments based on real data in road networks.
Volume 11 | 04-Special Issue
Pages: 1754-1767