CFP last date
20 March 2024
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

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.

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
%N 2
%P 16-20
%D 2013
%I International Journal of Computer Applications

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.

  1. Tseng, V, et al. , Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases,IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2013, vol. 25, pp. 1 - 15.
  2. Boaddh J, et al. , Empirical Evaluation of Bit Mask Search for Mining Frequent Item Sets, International Journal of Engineering and Innovative Technology (IJEIT), 2012, vol. 2, no. 6, pp. 1-15.
  3. Tseng VS, et al. , "UP-Growth: an efficient algorithm for high utility itemset mining," in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, pp. 253-262.
  4. Abaya SA, Association Rule Mining based on Apriori Algorithm in Minimizing Candidate Generation, International Journal of Scientific & Engineering Research Volume, 2012, vol. 3, pp. 1-4.
  5. Wang L, et al. , Accelerating probabilistic frequent itemset mining: a model-based approach, in Proceedings of the 19th ACM international conference on Information and knowledge management, 2010, pp. 429-438.
  6. Hong TP, et al. , Effective utility mining with the measure of average utility, Expert Systems with Applications, 2011, vol. 38, no. 7, pp. 8259-8265.
  7. Dr. Ramaraj E, A General Survey on Multidimensional And Quantitative Association Rule Mining Algorithms, International Journal of Engineering Research and Applications, 2013, vol. 3, no. 4, pp. 1442-1448.
  8. Ahmed CF, et al. , HUC-Prune: an efficient candidate pruning technique to mine high utility patterns, Applied Intelligence, 2011, vol. 34, no. 2, pp. 181-198.
  9. Y?ld?z B and Ergenç B, Comparison of two association rule mining algorithms without candidate generation, in Proceedings 10th IASTED international conference on artificial intelligence and applications, AIA, 2010, pp. 450-457.
  10. Zhang F, et al. , Accelerating frequent itemset mining on graphics processing units, The Journal of Supercomputing, 2013, pp. 1-24.
  11. Schlegel B, et al. , Memory-efficient frequent-itemset mining, in Proceedings of the 14th International Conference on Extending Database Technology, 2011, pp. 461-472.
  12. Ustundag A, et al. , Fuzzy rule-based system for the economic analysis of RFID investments, Expert Systems with Applications, 2010, vol. 37, no. 7, pp. 5300-5306.
  13. Kim Y, et al. , Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams, JIPS, 2010, vol. 6, no. 1, pp. 79-90.
  14. Roh GP, et al. , Supporting pattern-matching queries over trajectories on road networks, Knowledge and Data Engineering, IEEE Transactions on, 2011, vol. 23, no. 11, pp. 1753-1758.
  15. Liu Y, et al. , Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2012, vol. 23, pp. 1-12
Index Terms

Computer Science
Information Sciences


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