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Researcher profile

Wensheng Gan

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. 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 …

  2. 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 …

  3. Temporal Fuzzy Utility Maximization with Remaining Measure

    2022 · arXiv (Cornell University)

    High utility itemset mining approaches discover hidden patterns from large amounts of temporal data. However, an inescapable problem of high utility itemset mining is that its discovered results hide the quantities of patterns, which causes …

  4. A Generic Algorithm for Top-K On-Shelf Utility Mining

    2022 · arXiv (Cornell University)

    On-shelf utility mining (OSUM) is an emerging research direction in data mining. It aims to discover itemsets that have high relative utility in their selling time period. Compared with traditional utility mining, OSUM can find …

  5. Towards Sequence Utility Maximization under Utility Occupancy Measure

    2022 · arXiv (Cornell University)

    The discovery of utility-driven patterns is a useful and difficult research topic. It can extract significant and interesting information from specific and varied databases, increasing the value of the services provided. In practice, the measure …

  6. GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning

    2025 · arXiv (Cornell University)

    The class imbalance problem refers to the disproportionate distribution of samples across different classes within a dataset, where the minority classes are significantly underrepresented. This issue is also prevalent in graph-structured data. Most graph neural …