A Frequent Trajectory Path Mining Using Bit Mask Search and UP Growth+ Algorithm

Mining is a great service entities in trajectory database that indicates to the exposure of entities with huge service like acquisition. The extensive number of contender entities degrades the mining achievement in terms of execution time and space stipulation. The position may become worse when the database consists of endless lengthy transactions or lengthy huge utility entity sets. In this paper, UP -Growth+ algorithm is consider, for mining huge utility entities with a set of adequate approaches for pruning contender entities. The previous algorithms do not contribute any compaction or compression mechanism with respect to density in bit vector regions. To raise the density in bit-vector the Bit Mask Search (BM Search) starts with an array list. From root node, a BM Search representation for each frequent pattern is designed which gives an acceptable compression and compaction in bit search measure than UP Growth+ algorithms. The comparative analysis of UP Growth+ and BM Search are described in this paper. An experimental result shows that BM search produces better result than UP Growth + algorithm.