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High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm

by Anu Augustin, Vince Paul, Vishnu G. Nair
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 165 - Number 3
Year of Publication: 2017
Authors: Anu Augustin, Vince Paul, Vishnu G. Nair
10.5120/ijca2017913813

Anu Augustin, Vince Paul, Vishnu G. Nair . High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm. International Journal of Computer Applications. 165, 3 ( May 2017), 17-22. DOI=10.5120/ijca2017913813

@article{ 10.5120/ijca2017913813,
author = { Anu Augustin, Vince Paul, Vishnu G. Nair },
title = { High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 165 },
number = { 3 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume165/number3/27552-2017913813/ },
doi = { 10.5120/ijca2017913813 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:11:23.690252+05:30
%A Anu Augustin
%A Vince Paul
%A Vishnu G. Nair
%T High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 165
%N 3
%P 17-22
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

High utility itemset mining is an uncommon term. But we are using it while we are doing online purchases etc. It is a part of business analytics. Its main application area is market basket analysis where when a customer purchases an item he can buy another item to maximize profit. So both the customer and business vendors earn profit. This one is not a new concept and is derived from frequent itemset mining. Here we proposes an algorithm for mining closed high utility itemset using top-k algorithm. So that execution time will be less and space efficiency can also be achieved. Both the concept of closed high utility itemset and top-k mining are existing. The new concept is that integrating the merits of them together. The algorithm used for closed hui mining is CHUD.Similarly the algorithm used for top-k mining is TKU,TKO etc. Also recovering all HUIs from complete set of CHUIs using DAHU algorithm.

References
  1. Vincent S.Tseng, Cheng-Wei Wu, and Philippe Fournier- Viger,PhilipS.Yu. 2016 Efficient Algorithms for Mining Top-k High Utility Itemset. IEEE Transactions on Knowledge and Data Eng.,1-13.
  2. Vincent S.Tseng, Cheng-Wei Wu, and Philippe Fournier- Viger,PhilipS.Yu. 2016 Efficient Algorithms for Mining Top-k High Utility Itemsets. IEEE Transactions on Knowledge and Data Eng.,1-13.
  3. Vincent S.Tseng, Bai-En Shie,Cheng-Wei Wu and Philip S.Yu. 2013 Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases. IEEE Transactions on Knowledge and Data Engineering,1772- 1786.
  4. R. Agrawal and R. Srikant. 1994 Fast Nearest Neighbor Search with keywords. In Proc. of Int'l Conf. on Very Large Data Bases, 487-499.
  5. G. Pyun and U. Yun. 2014 Mining Top-K Frequent Patterns with Combination Reducing Techniques. Applied Intelligence,76-98.
  6. J. Han, J. Pei and Y. Yin. 2000 Mining Frequent Patterns without Candidate Generation. ,In Proc. of ACM SIGMOD Int'l Conf. on Management of Data, 1-12.
  7. Vincent S.Teng,Cheng-Wei Wu,Bai-En Shie and Philip S.Yu . 2010 UP Growth : An efficient algorithm for High Utility Itemset Mining. In Proc. of the ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, 253-262.
  8. H. Ryang and U. Yun. 2015 Top-K High Utility Pattern Mining with Effective Threshold Raising Strategies. Knowledge-Based Systems ,109-126.
  9. C. Wu, B. Shie, V. S. Tseng and P. S. Yu. 2012 Mining Top-K High Utility Itemsets. In Proc. Of the ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining,1776 - 7886.
  10. J. Yin, Z. Zheng, L. Cao, Y. Songand W.We. 2013 Mining Top-K High Utility Sequential Patterns. In Proc. of IEEE Int'l Conf. on Data Mining,1259-1264.
  11. Ting, I. H.; Kimble C; Kudenko. D. 2005. UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site's Design. In Proc. Of Intl.Conf. on Web Intelligence,179-185.
  12. Patrali Chatterjee, Donna L. Hoffman and Thomas P. Novak. 2003 Modeling the Clickstream: Implications for Web-Based Advertising Efforts. in Marketing Science, 520-541.
  13. Dam TL,Li K.,Fournier-Viger P.,Duong QH. 2016 An efficient algorithm for mining top-rank-k frequent patterns. Application Intelligence Springer 45(1),96-111.
  14. Wang JY,Han JW Lu Y.,Tzvetkov P. 2005 TFP:An efficient algorithm for mining top-k frequent closed itemsets. IEEE Transactions on Knowledge and Data Eng.17(5),652-664.
  15. Dam TL,Li K.,Fournier Viger P.,Duong OH. 2016 CLS-Miner:Efficient and Effective closed high utility itemset mining. Frontiers of Computer Science.Springer.
  16. M. J. Zaki and C. J. Hsiao.2005 “Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans. On Knowl. And Data Eng. 462–478.
  17. C. Lucchese, S. Orlando, and R. Perego. 2006 Fast and memory efficient mining of frequent closed itemsets. IEEE Trans. Knowl. Data Eng.,21–36.
  18. C.-W Wu, P. Fournier-Viger, P. S. Yu, and V. S. Tseng. 2011 Efficient mining of a concise and lossless representation of high utility itemsets. in Proc. IEEE Int. Conf. Data Mining,824–833.
  19. Mohammed J. Zaki, Ching-Jui Hsiao. CHARM:An efficient algorithm for closed mining. 2002 in Proceedings of SIAM intl. conf. on data mining,Society for industrial and applied Mathematics,457-473.
  20. B. Ganter and R. Wille. 1999 Formal Concept Analysis: Mathematical Foundations. Springer-Verlag.
  21. M. J. Zaki. 2000 Generating non-redundant association rules. In 6th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining.
  22. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. 1999 Discovering frequent closed itemsets for association rules. In 7th Intl. Conf. on Database Theory.
  23. 23] C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, and Y.-K. Lee. 2009 Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng., 1708– 1721.
  24. Cheng-Wei Wu, Philippe Fournier-Viger, Jia-Yuan Gu, Vincent S. Tseng. 2015 Mining Closed+ High Utility Itemsets without Candidate Generation. In IEEE Conference on Technologies and Applications of Artificial Intelligence (TAAI).
  25. The full wiki http://www.thefullwiki.org
Index Terms

Computer Science
Information Sciences

Keywords

Minimum Utility Threshold CHUD Top-K TWU Support count.

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