Results 201 to 210 of about 719,228 (260)

An efficient algorithm for fuzzy frequent itemset mining

Journal of Intelligent & Fuzzy Systems, 2020
Association-rule mining (ARM) has concerned as an important and critical research issue in the field of data analytics and mining that aims at finding the correlations among the items in binary databases.
Tsu-Yang Wu, Chun-Wei Lin, Unil Yun, Chun-Hao Chen, Gautam Srivastava, Xianbiao Lv   +5 more
semanticscholar   +1 more source

HIGH UTILITY ITEMSETS MINING

International Journal of Information Technology & Decision Making, 2010
High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web ...
Ying Liu 0039, Jianwei Li, Wei-keng Liao, Alok N. Choudhary, Yong Shi 0001   +4 more
openaire   +3 more sources

TopHUI: Top-k high-utility itemset mining with negative utility

2020 IEEE International Conference on Big Data (Big Data), 2020
In the field of data science, utility-driven data mining has become an emergent intelligent technique with wide applications. The existing utility mining algorithms usually discover all the patterns satisfying a given minimum utility threshold.
Wensheng Gan, Shicheng Wan, Jiahui Chen, Chien-Ming Chen, Lina Qiu   +4 more
semanticscholar   +1 more source

Symmetries in Itemset Mining

2012
In this paper, we describe a new framework for breaking symmetries in itemset mining problems. Symmetries are permutations between items that leave invariant the transaction database. Such kind of structural knowledge induces a partition of the search space into equivalent classes of symmetrical itemsets.
Jabbour, Said, Saïs, Lakhdar, Salhi, Yakoub, Tabia, Karim   +3 more
openaire   +2 more sources

Efficient Skyline Frequent-Utility Itemset Mining Algorithm on Massive Data

IEEE Transactions on Knowledge and Data Engineering
Frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are two important branches of itemset mining which is a key technology of knowledge discovery in many applications.
Jingxuan He, Xixian Han, Xiaolong Wan, Jinbao Wang   +3 more
semanticscholar   +1 more source

High-Utility Itemset Mining with Effective Pruning Strategies

ACM Transactions on Knowledge Discovery from Data, 2019
High-utility itemset mining is a popular data mining problem that considers utility factors, such as quantity and unit profit of items besides frequency measure from the transactional database.
J. Wu, Chun-Wei Lin, A. Tamrakar
semanticscholar   +1 more source

A BPSO-based method for high-utility itemset mining without minimum utility threshold

Knowledge-Based Systems, 2020
High-utility itemset mining is used to obtain high utility itemsets by taking into account both the quantity as well as the utility of each item, which have not been considered in frequent itemset mining.
Ridowati Gunawan, E. Winarko, Reza Pulungan   +2 more
semanticscholar   +1 more source

Practical Privacy-Preserving Frequent Itemset Mining on Supermarket Transactions

IEEE Systems Journal, 2020
Data mining is widely applied to establish connections among the items in massive datasets nowadays. Association rule mining is one of the most popular methods to perform data mining, and a fundamental part of this is frequent itemset mining.
Chenyang Ma, Baocang Wang, Kyle Jooste, Zhili Zhang, Yuan Ping   +4 more
semanticscholar   +1 more source

Mining Cohesive Itemsets in Graphs

2014
Discovering patterns in graphs is a well-studied field of data mining. While a lot of work has already gone into finding structural patterns in graph datasets, we focus on relaxing the structural requirements in order to find items that often occur near each other in the input graph.
Tayena Hendrickx, Boris Cule, Bart Goethals   +2 more
openaire   +2 more sources

Efficient Skyline Itemsets Mining

Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering - C3S2E '15, 2008
Utility Mining (UM) in context of Market Basket Analysis consists of mining itemsets from a transaction database guided by optimizing utility. For example, UM consists of extracting all itemsets in a transaction database having utility above a user-defined minimum threshold or mining Top-K high utility itemset.
Vikram Goyal, Ashish Sureka, Dhaval Patel 0002   +2 more
openaire   +1 more source