An Improved Approach For High Utility Itemset Mining Using Length Reduction Method

Authors(2) :-Afrin Shaikh, Vishal Shah

Data mining is process of analyzing data from different data repository and mine Useful and interesting patterns from them. It is also defined as the use of algorithm to discover hidden patterns and interesting relationship between large itemset. High utility itemset mining is an area research where utility based mining can be done. Mining high utility itemset from a transactional database refers to the discovery of itemset with high utility in a terms like weight, unit profit or value. High-utility item set mining (HUIM) is an important data mining task that refers to the set of items with high utility like profit in a customer transaction database. However an Important issue with traditional HUI mining algorithm is that they tend to find itemset having many items which increases memory and time overhead. To discover HUIs efficiently with length constraints, FHM+ introduced the concept of estimated utility co-occurrence structure (EUCS) and two Length Upper Bound Reduction (LUR) of itemset. EUCS has matrix structure and in that half of the matrix is not filled with data so it has memory overhead.. In this paper, we address this issue by presenting an improved algorithm based on tree data structure which can decreases the execution time and memory usage for HUI mining.

Authors and Affiliations

Afrin Shaikh
M.E Student, Department of Computer Engineering, Sardar Vallabhbhai Patel Institute of Technology, VASAD, India
Vishal Shah
Assistant Professor, Department of Computer Engineering, Sardar Vallabhbhai Patel Institute of Technology, VASAD, India

High Utility, HUI_Miner, Transactional Database, Transaction Weight Utilization (TWU), FHM

  1. Philippe Viger, Cheng Wu, Souleymane Zida, Vincent S. Tseng- "FHM: Faster High Utiltiy Mining Itemset mining Using Estimated Utility Co-occurrence purning." , Springer International, Switzerland 2014,ISMIS 2014.
  2. Philippe Fournier-Viger, Jerry Chun-Wei Lin,Quang-Huy Duong, Thu-Lan Dam, "FHM+: Faster High-Utility Itemset Mining using Length Upper-Bound Reduction.", Springer International Publishing Switzerland 2016.
  3. Souleymane Zida, Philippe Fournier-Viger(B), Jerry Chun-Wei Lin, Cheng-Wei Wu, and Vincent S. Tseng- "EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining.", Springer International Publishing Switzerland 2015.
  4. Sunidhi Shrivastava, Punit Kumar Johari " Analysis on High Utility Infrequent ItemSets Mining Over Transactional Database.", IEEE International Conference On Recent Trends In Electronics Information Communication Technology, May, 2016.
  5. Menghchi Liu, Junfeng Qu-"Mining High Utility Itemsets Without Candidate Generation", CIKM, Maui. USA, Nov 2012. 
  6. Shiming Guo, Hong Gao- "An Efficient Algorithm For Incremental and Interactive High Utility Itemset Mining" ,International Conference on Image, 2017.
  7. Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-pei Hong "Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds",C3S2E 2015.
  8. Thang Mai, Bay Vo, Loan T.T. Nguyen "A Lattice-based approach for mining high utility association rules", Information science Elsevier,2017.
  9. Vincent S. Tseng, Bai-En Shie, Cheng Wu, philip S. Yu- "Efficient Algorithms For Mining High Utility Itemset from Transactional Databases.", IEEE transactions on knowledge and data engineering (Vol 25, no. 8), Aug 2013, ISSN No: 1041-4347, DOI: 10.1109/TKED.2012.
  10. Jerry Chun-Lin, Member, IEEE, Shifeng Ren, Philippe Fournier-Viger Tzung-Pei Hong,"EHAUPM: Efficient High Average-Utility Pattern Mining with Tighter Upper-Bounds"IEEE 2016
  11. Junqiang Liu, Member, IEEE, Ke Wang, Senior Member, IEEE, and Benjamin C.M. Fung, Senior Member, IEEE " Mining High Utility Patterns in one phase without generating candidates" IEEE 2015.
  12. Philippe Fournier-Viger, Jerry Chun-Wei Lin, Ted Gueniche, Prashant Barhate, "Efficient Incremental High Utility Itemset Mining" ACM 2015.
  13. Guo-Cheng Lan , Tzung-Pei Hong , Vincent S. Tseng "An efficient projection-based indexing approach for Mining High Utility Itemsets" Springer 2013.
  14. Supachai Laoviboon, Komate Amphawan, "Mining High-Utility Itemsets with Irregular Occurrence" IEEE 2017.
  15. Wei Song, jaipei Xu "Discovering High Utility Itemset Using Map Reduce" IEEE 2016.
  16. P.Payal Swamy, Amit Pimpalkar " Improving method for graphical Analysis and representation of high utility itemsets using UP++ Growth. IEEE 2016.

Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 350-356
Manuscript Number : CSEIT1833570
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Afrin Shaikh, Vishal Shah, "An Improved Approach For High Utility Itemset Mining Using Length Reduction Method", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.350-356, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833570

Follow Us

Contact Us