Data Summarization with Hierarchical Taxonomy

Xuliang Zhu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


Data summarization has wide applications in real world, e.g. attributes filter, image set labeling and personalized recommendation. In this work, we study a new problem HSD to summarize a dataset using k concepts in a hierarchical taxonomy. Different from the existed works of whole hierarchy summarization, we focus on the accurate coverage of the given query set Q. The objective is to cover more items in Q and less items not in Q. To tackle it, we first propose a dynamic programming based algorithm on the tree hierarchy, which is a simple instance of HSD problem. Furthermore, we propose a heuristic method to assign the vertex to one of its in-neighbors for HDAGs and apply the tree algorithm on it. The experimental results confirm the quality of our methods on both tree and HDAG datasets.
Original languageEnglish
Title of host publicationSIGMOD '21: Proceedings of the 2021 International Conference on Management of Data
PublisherAssociation for Computing Machinery (ACM)
Number of pages3
ISBN (Print)9781450383431
Publication statusPublished - 18 Jun 2021
EventACM SIGMOD International Conference on Management of Data, SIGMOD 2021 - Virtual, Online, China
Duration: 20 Jun 202125 Jun 2021


ConferenceACM SIGMOD International Conference on Management of Data, SIGMOD 2021
Internet address


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