Results 131 to 140 of about 2,277 (164)

High-Utility Itemset Mining in Big Dataset

2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 2019
High-utility mining (HUIM) is an extended concept from frequent itemset mining (FIM). It emphasizes the more important factors, such as profits or the weight of an itemset in commercial applications. In this paper, we assume a dataset is too big to be loaded in the memory, then propose a MapReduce framework to handle this kind of situation, and try to ...
Jimmy Ming-Tai Wu, Jerry Chun-Wei Lin, Wei Min   +2 more
openaire   +1 more source

HMiner: Efficiently mining high utility itemsets

Expert Systems with Applications, 2017
Abstract High utility itemset mining problem uses the notion of utilities to discover interesting and actionable patterns. Several data structures and heuristic methods have been proposed in the literature to efficiently mine high utility itemsets.
openaire   +1 more source

Mining High Utility Itemsets over Uncertain Databases

2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2015
Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a large amount of uncertain data, i.e. RFID data, sensor data, real-time monitoring data, etc., has been collected. As one of the most fundamental issues of uncertain data mining, the problem of mining uncertain frequent item sets has attracted much attention in ...
Yuqing Lan, Yang Wang, Yanni Wang, Shengwei Yi, Dan Yu   +4 more
openaire   +1 more source

High-Utility Itemset Mining in Big Dataset

2020
High-utility mining (HUIM) is an extended concept from frequent itemset mining (FIM). It emphasizes the more important factors, such as profits or the weight of an itemset in commercial applications. In this paper, we assume a dataset is too big to be loaded in the memory, then propose a MapReduce framework to handle this kind of situation, and try to ...
Jimmy Ming-Tai Wu, Min Wei, Jerry Chun-Wei Lin, Chien-Ming Chen   +3 more
openaire   +1 more source

High-Utility Itemset Mining

2022
V. Jeevika Tharini, B.L. Shivakumar
openaire   +1 more source

Parallel High Utility Itemset Mining

2022
Gaojuan Fan, Huaiyuan Xiao, Chongsheng Zhang, George Almpanidis, Philippe Fournier-Viger, Hamido Fujita   +5 more
openaire   +1 more source

Pruning strategies for mining high utility itemsets

Expert Systems with Applications, 2015
Presents an efficient high utility mining method.Employs novel pruning strategies to limit the search space of utility mining.Compares the proposed method against a state-of-the-art utility mining method.Experimentally evaluates the system on eight real and synthetic benchmark datasets.Empirical results are found to be quite promising, especially for ...
openaire   +1 more source

Mining High Utility Itemsets from Multiple Databases

2018
In the past, many algorithms have been developed to efficiently mine the high-utility itemsets from a single data source, which is not a realistic scenario since the data may be distributed into varied branches, and the discovered information should be integrated together for making the effective decision.
Jerry Chun-wei Lin, Yuanfa Li, Philippe Fournier-Viger, Linlin Tang   +3 more
openaire   +1 more source

Utility-Oriented Gradual Itemsets Mining Using High Utility Itemsets Mining

2023
Fongue, Audrey, Lonlac, Jerry, Tsopze, Norbert   +2 more
openaire   +2 more sources

Targeted Querying of Closed High-Utility Itemsets

2023 IEEE International Conference on Big Data (BigData), 2023
Shan Huang, Wensheng Gan, Jinbao Miao
openaire   +1 more source