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The Scientific World Journal
Volume 2015, Article ID 161564, 15 pages
http://dx.doi.org/10.1155/2015/161564
Research Article

An Incremental High-Utility Mining Algorithm with Transaction Insertion

1School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus, Shenzhen University Town, Xili, Shenzhen 518055, China
2Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan
3Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
4Medical School, Shenzhen University, Shenzhen 518060, China

Received 30 July 2014; Revised 21 August 2014; Accepted 14 September 2014

Academic Editor: Zheng Xu

Copyright © 2015 Jerry Chun-Wei Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [11 citations]

The following is the list of published articles that have cited the current article.

  • Nourhan N. Abuzayed, and Belgin Ergenç, “Comparison of Dynamic Itemset Mining Algorithms for Multiple Support Thresholds,” Proceedings of the 21st International Database Engineering & Applications Symposium on - IDEAS 2017, pp. 309–316, . View at Publisher · View at Google Scholar
  • Unknown, Aruna Govada, Abhinav Patluri, and Atmika Honnalgere, “Association Rule Mining using Apriori for Large and Growing Datasets under Hadoop,” Proceedings of the 2017 VI International Conference on Network, Communication and Computing - ICNCC 2017, pp. 14–17, . View at Publisher · View at Google Scholar
  • Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, and Vincent S. Tseng, “Efficiently mining uncertain high-utility itemsets,” Soft Computing, vol. 21, no. 11, pp. 2801–2820, 2016. View at Publisher · View at Google Scholar
  • Geetha, and Kavitha, “Review on high utility itemset mining algorithms,” IEEE WCTFTR 2016 - Proceedings of 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare, 2016. View at Publisher · View at Google Scholar
  • Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, and Vincent S. Tseng, “Efficient Algorithms for Mining High-Utility Itemsets in Uncertain Databases,” Knowledge-Based Systems, 2016. View at Publisher · View at Google Scholar
  • Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, and Justin Zhan, “Data mining in distributed environment: a survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, pp. e1216, 2017. View at Publisher · View at Google Scholar
  • Heungmo Ryang, Hamido Fujita, Unil Yun, and Gangin Lee, “An efficient algorithm for mining high utility patterns from incremental databases with one database scan,” Knowledge-Based Systems, vol. 124, pp. 188–206, 2017. View at Publisher · View at Google Scholar
  • Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Han-Chieh Chao, and Tzung-Pei Hong, “Efficiently mining frequent itemsets with weight and recency constraints,” Applied Intelligence, 2017. View at Publisher · View at Google Scholar
  • Rajendra Prasad, “Optimized high-utility itemsets mining for effective association mining paper,” International Journal of Electrical and Computer Engineering, vol. 7, no. 5, pp. 2911–2918, 2017. View at Publisher · View at Google Scholar
  • Philippe Fournier-Viger, Hamido Fujita, Han-Chieh Chao, Tzung-Pei Hong, Jerry Chun-Wei Lin, and Wensheng Gan, “A survey of incremental high-utility itemset mining,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 2, 2018. View at Publisher · View at Google Scholar
  • Giuseppe DrAniello, Matteo Gaeta, and Tzung-Pei Hong, “Effective Quality-Aware Sensor Data Management,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 65–77, 2018. View at Publisher · View at Google Scholar