CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning-Reference-Cited by-同舟云学术

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CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning

Published:2019-05-01 Issue:5 Volume:31 Page:853-866
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Jian Songlei^ORCID,Pang Guansong,Cao Longbing^ORCID,Lu Kai,Gao Hang

Funder

National Key Research and Development Program of China

National Science Foundation

HUNAN Province Science Foundation

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/8680720/08395013.pdf?arnumber=8395013

Cited by 36 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning;2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS);2024-07-23

2. HyperCARS: Using Hyperbolic Embeddings for Generating Hierarchical Contextual Situations in Context-Aware Recommender Systems;Information Systems Research;2024-07-08

3. Categorical Data Clustering: A Bibliometric Analysis and Taxonomy;Machine Learning and Knowledge Extraction;2024-05-07

4. Deep Coupling Network for Multivariate Time Series Forecasting;ACM Transactions on Information Systems;2024-04-27

5. Towards machine learned generative design;Automation in Construction;2024-03

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