Access the full text.
Sign up today, get DeepDyve free for 14 days.
Pankaj Singh, Sudhakar Singh, P. Mishra, Rakhi Garg (2019)
RDD-Eclat: Approaches to Parallelize Eclat Algorithm on Spark RDD FrameworkOther Information Systems & eBusiness eJournal
Hong Yao, Howard Hamilton, C. Butz (2004)
A Foundational Approach to Mining Itemset Utilities from Databases
V. Tseng, Bai-En Shie, Cheng-Wei Wu, Philip Yu (2013)
Efficient Algorithms for Mining High Utility Itemsets from Transactional DatabasesIEEE Transactions on Knowledge and Data Engineering, 25
Stephan Borzsony, Donald Kossmann, Konrad Stocker (2001)
The skyline operatorInternational Conference on Data Engineering, 5
Bart Goethals, M. J. Zaki (2003)
Frequent itemset mining implementations repositoryRetrieved from http://fimi.cs.helsinki.fi.
Wensheng Gan, Chun-Wei Lin, H. Chao, J. Zhan (2017)
Data mining in distributed environment: a surveyWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7
Vikram Goyal, A. Sureka, D. Patel (2015)
Efficient Skyline Itemsets MiningProceedings of the Eighth International C* Conference on Computer Science & Software Engineering
(2000)
Example database Foodmart of Microsoft Analysis Services
Xuemin Lin, Yidong Yuan, Qing Zhang, Ying Zhang (2007)
Selecting Stars: The k Most Representative Skyline Operator2007 IEEE 23rd International Conference on Data Engineering
Jan Chomicki, Parke Godfrey, Jarek Gryz, Dongming Liang (2003)
Skyline with presortingInternational Conference on Data Engineering
Gautam Srivastava, Jerry Lin, Matin Pirouz, Yuanfa Li, Unil Yun (2021)
A Pre-Large Weighted-Fusion System of Sensed High-Utility PatternsIEEE Sensors Journal, 21
Raymond Chan, Qiang Yang, Yi-Dong Shen (2003)
Mining high utility itemsetsThird IEEE International Conference on Data Mining
Junqiang Liu, Yunhe Pan, Ke Wang, Jiawei Han (2002)
Mining frequent item sets by opportunistic projectionProceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mohammed Zaki, S. Parthasarathy, M. Ogihara, Wei Li (1997)
New Algorithms for Fast Discovery of Association Rules
Jieh-Shan Yeh, Yu-Chiang Li, Chinchen Chang (2007)
Two-Phase Algorithms for a Novel Utility-Frequent Mining Model
Wensheng Gan, Chun-Wei Lin, Philippe Fournier-Viger, H. Chao, Philip Yu (2018)
A Survey of Parallel Sequential Pattern MiningACM Transactions on Knowledge Discovery from Data (TKDD), 13
D. Papadias, Yufei Tao, Greg Fu, B. Seeger (2005)
Progressive skyline computation in database systemsACM Trans. Database Syst., 30
Bart Goethals, Mohammed Zaki (2004)
Advances in frequent itemset mining implementations: report on FIMI'03SIGKDD Explor., 6
J. Chomicki, P. Godfrey, Jarek Gryz, Dongmin Liang (2003)
Skyline with presortingProceedings 19th International Conference on Data Engineering (Cat. No.03CH37405)
Jeng-Shyang Pan, Chun-Wei Lin, Lu Yang, Philippe Fournier-Viger, T. Hong (2017)
Efficiently mining of skyline frequent-utility patternsIntell. Data Anal., 21
Jiawei Han, J. Pei, Yiwen Yin (2000)
Mining frequent patterns without candidate generation
Vid Podpečan, N. Lavrač, I. Kononenko (2007)
A Fast Algorithm for Mining Utility-Frequent Itemsets
Ashok Savasere, E. Omiecinski, S. Navathe (1995)
An Efficient Algorithm for Mining Association Rules in Large Databases
Mohammed Zaki (2000)
Scalable Algorithms for Association MiningIEEE Trans. Knowl. Data Eng., 12
WuJimmy Ming-Tai, LinJerry Chun-Wei, TamrakarAshish (2019)
High-Utility Itemset Mining with Effective Pruning Strategies
Bart Goethals, Mohammed Zaki (2003)
Advances in Frequent Itemset Mining Implementations: Introduction to FIMI03
V. Tseng, Cheng-Wei Wu, Philippe Fournier-Viger, Philip Yu (2016)
Efficient Algorithms for Mining Top-K High Utility ItemsetsIEEE Transactions on Knowledge and Data Engineering, 28
K. Tan, P. Eng, B. Ooi (2001)
Efficient Progressive Skyline Computation
Jong Park, Ming-Syan Chen, Philip Yu (1995)
An effective hash-based algorithm for mining association rules
Usman Ahmed, Jerry Lin, Gautam Srivastava, Rizwan Yasin, Y. Djenouri (2021)
An Evolutionary Model to Mine High Expected Utility Patterns From Uncertain DatabasesIEEE Transactions on Emerging Topics in Computational Intelligence, 5
Mengchi Liu, Jun-Feng Qu (2012)
Mining high utility itemsets without candidate generationProceedings of the 21st ACM international conference on Information and knowledge management
Jong Soo Park, Ming-Syan Chen, Philip S. Yu (1995)
An effective hash-based algorithm for mining association rulesACM SIGMOD Rec., 24
Chun-Wei Lin, Lu Yang, Philippe Fournier-Viger, T. Hong (2019)
Mining of skyline patterns by considering both frequent and utility constraintsEng. Appl. Artif. Intell., 77
Philippe Fournier-Viger, Cheng-Wei Wu, V. Tseng (2012)
Mining Top-K Association Rules
Philippe Fournier-Viger, Jerry Lin, R. Kiran, Yun Koh, Rincy Thomas (2017)
A Survey of Sequential Pattern Mining
Philippe Fournier-Viger, Chun-Wei Lin, Bay Vo, Tin Truong, Ji Zhang, H. Le (2017)
A survey of itemset miningWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7
Rakesh Agrawal, Tomasz Imieliński, Arun Swami (1993)
Mining association rules between sets of items in large databasesACM SIGMOD International Conference on Management of Data
Donald Kossmann, Frank Ramsak, S. Rost (2002)
Shooting Stars in the Sky: An Online Algorithm for Skyline Queries
S. Börzsönyi, Donald Kossmann, K. Stocker (2001)
The Skyline operatorProceedings 17th International Conference on Data Engineering
Chee-Yong Chan, H. Jagadish, K. Tan, A. Tung, Zhenjie Zhang (2006)
Finding k-dominant skylines in high dimensional spaceProceedings of the 2006 ACM SIGMOD international conference on Management of data
R. Agrawal, R. Srikant (1998)
Fast Algorithms for Mining Association Rules
J. Wu, Chun-Wei Lin, Ashish Tamrakar (2019)
High-Utility Itemset Mining with Effective Pruning StrategiesACM Transactions on Knowledge Discovery from Data (TKDD), 13
Wensheng Gan, Chun-Wei Lin, Philippe Fournier-Viger, H. Chao, V. Tseng, Philip Yu (2018)
A Survey of Utility-Oriented Pattern MiningIEEE Transactions on Knowledge and Data Engineering, 33
Y. Liu, W. Liao, A. Choudhary (2005)
A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets
V. Tseng, Cheng-Wei Wu, Bai-En Shie, Philip Yu (2010)
UP-Growth: an efficient algorithm for high utility itemset miningProceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
R. Agrawal, T. Imielinski, A. Swami (1993)
Mining association rules between sets of items in large databasesProceedings of the 1993 ACM SIGMOD international conference on Management of data
Donald Kossmann, Frank Ramsak, Steffen Rost (2002)
Shooting stars in the sky: An online algorithm for skyline queriesInternational Conference on Very Large Data Bases
Wensheng Gan, Jerry Lin, Jiexiong Zhang, Philip Yu (2019)
Utility Mining across Multi-Sequences with Individualized ThresholdsACM Transactions on Data Science, 1
Pankaj Singh, Sudhakar Singh, P. K. Mishra, Rakhi Garg (2019)
RDD-Eclat: Approaches to parallelize Eclat algorithm on Spark RDD frameworkInternational Conference on Computer Networks and Inventive Communication Technologies
Chun-Wei Lin, T. Hong, Wen-Hsiang Lu (2011)
An effective tree structure for mining high utility itemsetsExpert Syst. Appl., 38
Tsu-Yang Wu, Chun-Wei Lin, Unil Yun, Chun-Hao Chen, Gautam Srivastava, Xianbiao Lv (2020)
An efficient algorithm for fuzzy frequent itemset miningJ. Intell. Fuzzy Syst., 38
Jieh-Shan Yeh, Yu-Chiang Li, Chin-Chen Chang (2007)
Two-phase algorithms for a novel utility-frequent mining modelPacific-Asia Conference on Knowledge Discovery and Data Mining
In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspects of frequency and utility has been extensively discussed. In most cases, however, people tend to focus on purchase quantities of itemsets rather than frequencies. In this article, we propose a new knowledge called skyline quantity-utility pattern (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms, respectively, called SQU-Miner and SKYQUP are presented to efficiently mine the set of SQUPs. Moreover, the usage of volunteer computing is proposed to show the potential in real supermarket applications. Two new efficient utility-max structures are also mentioned for the reduction of the candidate itemsets, respectively, utilized in SQU-Miner and SKYQUP. These two new utility-max structures are used to store the upper-bound of utility for itemsets under the quantity constraint instead of frequency constraint, and the second proposed utility-max structure moreover applies a recursive updated process to further obtain strict upper-bound of utility. Our in-depth experimental results prove that SKYQUP has stronger performance when a comparison is made to SQU-Miner in terms of memory usage, runtime, and the number of candidates.
ACM Transactions on Internet Technology (TOIT) – Association for Computing Machinery
Published: Jul 16, 2021
Keywords: Skyline quantity-utility patterns (SQUPs)
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.