A5046518265- Data Mining Algorithms and Applications
- Rough Sets and Fuzzy Logic
- Data Management and Algorithms
- Imbalanced Data Classification Techniques
- Remote Sensing and Land Use
- Privacy-Preserving Technologies in Data
- Animal Ecology and Behavior Studies
- Forest Management and Policy
- Air Quality Monitoring and Forecasting
- Ethics and Social Impacts of AI
- Peer-to-Peer Network Technologies
- Environmental Quality and Pollution
- Regional Economic and Spatial Analysis
- Wildlife Ecology and Conservation
- Environmental Changes in China
- Air Quality and Health Impacts
- Climate Change and Health Impacts
- Privacy, Security, and Data Protection
- Yersinia bacterium, plague, ectoparasites research
Monash University
2023
Shandong University of Science and Technology
2020-2022
Huawei Technologies (China)
2003
Informations sur les effectifs, le regime alimentaire, l'activite, l'hibernation et l'utilisation de l'espace l'ours noir asiatique dans reserves naturelles Wolong Tangjiahe au Sichuan
Rapid advancement of industrial internet things (IoT) technology has changed the supply chain network to an open system meet high demand for individualized products and provide better customer experiences. However open-system forced many small midsize enterprises (SMEs) adopt vertical integration by being divided into smaller companies with a distinctive business each SME but central alliance produce range gain competencies. Therefore, existing models do not guarantee protection data privacy...
High average-utility itemset mining (HAUIM) is an extension of high-utility (HUIM), which provides a reliable measure to reveal utility patterns by considering the length mined pattern. Some research has been conducted improve efficiency designing variety pruning strategies and effective frameworks, but few works have focused on maintenance algorithms in dynamic environment. Unfortunately, most existing HAUIM still rescan databases multiple times when it necessary. In this paper, pre-large...
HAUIM (High Average-Utility Itemset Mining) is a variation of HUIM (High-Utility that provides reliable measure to reveal utility patterns in light the length mined pattern. Several works have been studied improve mining efficiency by designing multiple pruning strat egies and efficient frameworks, but fewer studies centered on sophisticated database maintenance algorithm. Existing still rescan databases times when it necessary. We first use pre-large principle this paper efficiently update...
During the past several years, revealing some useful knowledge or protecting individual’s private information in an identifiable health dataset (i.e., within Electronic Health Record) has become a tradeoff issue. Especially this era of global pandemic, security and privacy are often overlooked lieu usability. Privacy preserving data mining (PPDM) is definitely going to be have important role resolve problem. Nevertheless, scenario holds high complexity compared traditional PPDM problems....
In the ever-growing world, concepts of High-utility Itemset Mining (HUIM) as well Frequent (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these only used one factor to estimate an itemset. past, skyline pattern mining by considering both aspects frequency and utility has extensively discussed. most cases, however, people tend focus on purchase quantities itemsets rather than frequencies. this article, we propose a new called...
Abstract The high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility (HUIM) for revealing the satisfied and interesting patterns. In practical applications, database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed handle insertion process but fewer studies focused processing deletion knowledge maintenance. this paper, we then develop PRE-HAUI-DEL algorithm that...
In this paper, we propose a new pattern called skyline quantity-utility (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms respectively SQUM-1 SQUM-2 are presented efficiently mine set of SQUPs. efficient utility-max structures also mentioned for reduction candidate itemsets utilized two developed algorithms. Our in-depth experimental results prove that our proposed achieve good performance terms runtime memory usage.