Jerry Chun‐Wei Lin
11 papers in the PaperMetrix corpus
Papers by this author
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Maintenance of Discovered High Average-Utility Itemsets in Dynamic Databases
2018 · Applied Sciences
High-utility itemset mining (HUIM) is an extension of traditional frequent itemset mining, which considers both quantities and unit profits of items in a database to reveal highly profitable itemsets regardless of their size. High average-utility …
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Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow
2019 · IEEE Access
Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area …
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Utility Mining Across Multi-Dimensional Sequences
2019 · arXiv (Cornell University)
Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented …
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Utility Mining across Multi-Sequences with Individualized Thresholds
2020 · ACM/IMS Transactions on Data Science
Utility-oriented pattern mining is an emerging topic, since it can reveal high-utility patterns from different types of data, which provides more information than the traditional frequency/confidence-based pattern mining models. The utilities of various items/objects are …
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Comparative Study on Trajectory Outlier Detection Algorithms
2019
This paper explores, reviews, and evaluates the existing outlier trajectory detection using small, large and big databases. We divide existing solutions into two main categories: similarity-based, and clustering-based approaches. The first category groups solutions employing …
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Uncertain-Driven Analytics of Sequence Data in IoCV Environments
2020 · IEEE Transactions on Intelligent Transportation Systems
As the increasing availability and use of dynamic mobile communications, information from an Internet of Things (IoT) subset of devices, known as Internet of Connected Vehicles (IoCV), is collected with a level of uncertainty. To …
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Large-Scale High-Utility Sequential Pattern Analytics in Internet of Things
2020 · IEEE Internet of Things Journal
The concepts of sequential pattern mining have become a growing topic in data mining, finding a home most recently in the Internet of Things (IoT) where large volumes of data are presented by the second …
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Emergent Deep Learning for Anomaly Detection in Internet of Everything
2021 · IEEE Internet of Things Journal
This research presents a new generic deep learning (DL) framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE …
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Temporal positional lexicon expansion for federated learning based on hyperpatism detection
2022 · Expert Systems
Abstract Internet‐based information exchange has resulted in the propagation of false and misleading information, which is highly detrimental to individuals and humankind. Due to the speed and volume of social media news production, supervised artificial …
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An Approach to Semantic-Aware Heterogeneous Network Embedding for Recommender Systems
2023 · IEEE Transactions on Cybernetics
Recent studies on heterogeneous information network (HIN) embedding-based recommendations have encountered challenges. These challenges are related to the data heterogeneity of the associated unstructured attribute or content (e.g., text-based summary/description) of users and items in …
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A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences
2024 · arXiv (Cornell University)
In rapidly evolving e-commerce industry, the capability of selecting high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model, a utility mining-based active learning strategy …