CFP last date
20 May 2024
Reseach Article

A Conceptual Approach to Temporal Weighted Itemset Utility Mining

by Jyothi Pillai, O.P. Vyas, Sunita Soni, Maybin Muyeba
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 28
Year of Publication: 2010
Authors: Jyothi Pillai, O.P. Vyas, Sunita Soni, Maybin Muyeba
10.5120/510-827

Jyothi Pillai, O.P. Vyas, Sunita Soni, Maybin Muyeba . A Conceptual Approach to Temporal Weighted Itemset Utility Mining. International Journal of Computer Applications. 1, 28 ( February 2010), 55-60. DOI=10.5120/510-827

@article{ 10.5120/510-827,
author = { Jyothi Pillai, O.P. Vyas, Sunita Soni, Maybin Muyeba },
title = { A Conceptual Approach to Temporal Weighted Itemset Utility Mining },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 28 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 55-60 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number28/510-827/ },
doi = { 10.5120/510-827 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:43:18.910462+05:30
%A Jyothi Pillai
%A O.P. Vyas
%A Sunita Soni
%A Maybin Muyeba
%T A Conceptual Approach to Temporal Weighted Itemset Utility Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 28
%P 55-60
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Conventional Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. Rare objects are often of great interest and great value. Until recently, rarity has not received much attention in the context of data mining. For many real world applications, however, utility of rare itemsets based on cost, profit or revenue is of importance.

References
  1. Yao, Hong, Hamilton, H., and Butz, C. J. 2004. A Foundational Approach to Mining Itemset Utilities from Databases, Proceedings of the Third SIAM International Conference on Data Mining, Orlando, Florida, pp. 482-486.
  2. Chu, C., Tseng, V. S., and Liang, T. 2008. An efficient algorithm for mining temporal high utility itemsets from data streams. J. Syst. Softw. 81, 7 (Jul. 2008), 1105-1117
  3. Hu, J., Mojsilovic, A. High-utility Pattern Mining: A Method for Discovery of High-utility Item Sets, Pattern Recognition, Vol. 40, 3317-3324.
  4. Ale, J. M. and Rossi, G. H. (2000). An Approach to Discovering Temporal Association Rules. In Proceedings of the 2000 ACM Symposium on Applied Computing, Vol.1, J. Carroll, E. Damiani, H. Haddad, and D. Oppenheim, Eds. SAC ‘00. ACM Press, New York, NY, pp 294-300
  5. Yao, H. and Hamilton, H, J. 2006. Mining Itemset Utilities from Transaction Databases, Data and Knowledge Engineering, 59(3): 603-626
  6. Liu, Y., Liao, W., and Choudhray, A. 2005. A Fast High Utility Itemsets Mining Algorithm. Proceedings of the Utility-Based Data Mining Workshop.
  7. Teng, W. G., Chen, M. S., and Yu, P. S. 2003. A Regression-Based Temporal Pattern Mining Scheme for Data Streams. Proceedings of the 29th International Conference on Very Large Databases, pp 93-104.
  8. Ahmed, C. F., Tanbeer, S. K., Jeong, B-S, and Lee, Y-K. 2008. Handling Dynamic Weights in Weighted Frequent Pattern Mining, IEICE Trans. Information and Systems, Vol. E91-D:2578-2588.
  9. Han, J., Pei, J. and Yiwen, Y. 2000. Mining Frequent Patterns Without Candidate Generation. Proceedings ACM-SIGMOD International Conference on Management of Data, ACM Press, pp1-12.
  10. Coenen, F., Leng, P. and Ahmed, S. 2004. Data Structures for association Rule Mining: T-trees and P-trees. IEEE Transactions on Data and Knowledge Engineering, Vol 16, No 6, pp774-778
  11. Yun, U. 2007. Mining lossless closed frequent patterns with weight constraints. Know.-Based Syst. 20, 1 (Feb. 2007), 86-97
  12. Ning, H. and Yuan, S-C. 2006. Temporal Association Rules in Mining Method, First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS'06) pp. 739-742
  13. Verma, K., Vyas, O. P. and Vyas,R. 2005. Temporal Approach to Association Rule Mining Using T-Tree and P-Tree, Machine Learning and Data Mining in Pattern Recognition, 651-659, LNS Volume 3587
  14. Cheng-Yue Chang, Ming-Syan Chen, Chang-Hung Lee, Mining General Temporal Association Rules for Items with Different Exhibition Periods , Second IEEE International Conference on Data Mining (ICDM'02),
  15. Yo-Ping Huang; Li-Jen Kao; Sandnes, F.-E. , 2005. A prefix tree-based model for mining association rules from quantitative temporal data, Systems, Man and Cybernetics, 2005 IEEE International Conference on Volume 1, Issue , 10-12 Oct. 2005 Page(s): 158 - 163 Vol. 1
  16. Edi Winarko and John F. Roddick,. 2005. Discovering Richer Temporal Association Rules from Interval-Based Data, Data Warehousing and Knowledge Discovery, LNCS 3589, 315-325.
  17. Kriegel, H-P et al. 2007. Future Trends in Data Mining, Data Mining and Knowledge Discovery, 15:87–97
  18. Gaber, M. M,, Zaslavsky, A. and Krishnaswamy, S. 2005. Mining data streams: a review. SIGMODRecords
  19. Lu, S., Hu, H. and Li, F. 2005. Mining weighted association rules. Intelligent Data Analysis, 5(3):211–225.
  20. Agrawal ,R. Srikant, R. 1994. Fast Algorithms for Mining Association Rules, In: Proceedings of 20th International Conference on Very Large Databases, Santiago, Chile, pp. 487-499.
  21. Yingjiu Li, Peng Ning, X. Sean Wang , Sushil Jajodia R. 2003. Discovering calendar- based temporal association rules, Data & Knowledge Engineering volume 4,Elesvier publisher, Volume 44, pp 193-214.
  22. Agrawal R., Imielinski T., and Swami A., "Mining association rules between sets of items in large databases", Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, pages 207–216, 1993.
  23. Zhai Liang, Tang Xinming, Li Lin, Jiang Wenliang, Temporal Association Rule Mining based on T-Apriori Algorithm and its typical application, Proceedings of International Symposium on Spatio-temporal Modeling, Spatial Reasoning, Analysis, Data Mining and Data Fusion, 2005.
  24. Sushmita Mitra, Sankar K. Pal, Pabitra Mitra, Data Mining in Soft Computing Framework: A Survey, IEEE Transactions On Neural Networks, VOL. 13, NO. 1, January 2002
Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Mining Utility Temporal Frequent Pattern Mining Temporal Rare Itemset