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Reseach Article

Bit Mask Search Algorithm for Trajectory Database Mining

Published on December 2013 by P. Geetha, E. Ramaraj
International Conference on Computing and information Technology 2013
Foundation of Computer Science USA
IC2IT - Number 2
December 2013
Authors: P. Geetha, E. Ramaraj
205cb832-852c-46c2-893a-79701ddd4083

P. Geetha, E. Ramaraj . Bit Mask Search Algorithm for Trajectory Database Mining. International Conference on Computing and information Technology 2013. IC2IT, 2 (December 2013), 16-20.

@article{
author = { P. Geetha, E. Ramaraj },
title = { Bit Mask Search Algorithm for Trajectory Database Mining },
journal = { International Conference on Computing and information Technology 2013 },
issue_date = { December 2013 },
volume = { IC2IT },
number = { 2 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 16-20 },
numpages = 5,
url = { /proceedings/ic2it/number2/14395-1327/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Computing and information Technology 2013
%A P. Geetha
%A E. Ramaraj
%T Bit Mask Search Algorithm for Trajectory Database Mining
%J International Conference on Computing and information Technology 2013
%@ 0975-8887
%V IC2IT
%N 2
%P 16-20
%D 2013
%I International Journal of Computer Applications
Abstract

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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

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