Mining great service entities in trajectory database 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, we use two algorithms, namely Utility Pattern Growth (UP –Growth) 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 the density in bit vector regions. To raise the density in bit-vector sector the Bit search 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 algorithm. This paper compared two algorithms such as UP Growth and BM Search. In the analysis of two algorithms BM Search produces best result compared than the other algorithms. An experimental result shows the comparison of two algorithms.

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Utility Pattern Growth Bit Mask Search Trajectory Databases Frequent Entity Set.