Fuyuan Cao

J. Artif. Intell. Res., 2024

Towards Effective Graph Rationalization via Boosting Environment Diversity.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Towards Privacy-Aware Causal Structure Learning in Federated Setting.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, December, 2023

Local Causal Discovery in Multiple Manipulated Datasets.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., October, 2023

Evaluating Classification Model Against Bayes Error Rate.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

Learning Causal Representations for Robust Domain Adaptation.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., March, 2023

A relative labeling importance estimation algorithm based on global-local label correlations for multi-label learning.

[BibT_eX]

[DOI]

Yilu Liu

Appl. Intell., March, 2023

2022

Dual-Representation-Based Autoencoder for Domain Adaptation.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2022

Towards Efficient Local Causal Structure Learning.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, 2022

Centroids-guided deep multi-view K-means clustering.

[BibT_eX]

[DOI]

Jing Liu

Inf. Sci., 2022

Error-aware Markov blanket learning for causal feature selection.

[BibT_eX]

[DOI]

Inf. Sci., 2022

Weighted matrix-object data clustering guided by matrix-object distributions.

[BibT_eX]

[DOI]

Liqin Yu

Eng. Appl. Artif. Intell., 2022

Incomplete multi-view clustering via local and global co-regularization.

[BibT_eX]

[DOI]

Sci. China Inf. Sci., 2022

Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Instance Selection: A Bayesian Decision Theory Perspective.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Semi-Supervised Clustering With Constraints of Different Types From Multiple Information Sources.

[BibT_eX]

[DOI]

Liang Bai

IEEE Trans. Pattern Anal. Mach. Intell., 2021

k-Mnv-Rep: A k-type clustering algorithm for matrix-object data.

[BibT_eX]

[DOI]

Inf. Sci., 2021

An outlier detection algorithm for categorical matrix-object data.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2021

2020

A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters.

[BibT_eX]

[DOI]

Liang Bai

Inf. Fusion, 2020

Combining attribute content and label information for categorical data ensemble clustering.

[BibT_eX]

[DOI]

Appl. Math. Comput., 2020

A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

ContourGAN: Image contour detection with generative adversarial network.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2019

一种基于样本分层的双向过采样方法 (Bi-directional Oversampling Method Based on Sample Stratification).

[BibT_eX]

[DOI]

Xiaomin Zhou

Liqin Yu

计算机科学, 2019

基于谱聚类的二分网络社区发现算法 (Detecting Community from Bipartite Network Based on Spectral Clustering).

[BibT_eX]

[DOI]

Xiaoqin Zhang

Xiaodan An

计算机科学, 2019

融合多因素的兴趣点协同推荐方法研究 (Study on Point-of-interest Collaborative Recommendation Method Fusing Multi-factors).

[BibT_eX]

[DOI]

Jiong Chen

Hu Zhang

计算机科学, 2019

Embedded Representation of Relation Words with Visual Supervision.

[BibT_eX]

[DOI]

Proceedings of the 3rd IEEE International Conference on Robotic Computing, 2019

Online Social Information Propagation Analysis Based on Time-Delay Mixture Diffusion Model.

[BibT_eX]

[DOI]

Proceedings of the Fifth IEEE International Conference on Multimedia Big Data, 2019

2018

An Algorithm for Clustering Categorical Data With Set-Valued Features.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2018

A new distance with derivative information for functional k-means clustering algorithm.

[BibT_eX]

[DOI]

Inf. Sci., 2018

A sequential ensemble clusterings generation algorithm for mixed data.

[BibT_eX]

[DOI]

Xingwang Zhao

Appl. Math. Comput., 2018

On distances in vertex-weighted trees.

[BibT_eX]

[DOI]

Appl. Math. Comput., 2018

2017

k-mw-modes: An algorithm for clustering categorical matrix-object data.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2017

A fuzzy SV-k-modes algorithm for clustering categorical data with set-valued attributes.

[BibT_eX]

[DOI]

Appl. Math. Comput., 2017

2014

A simple and effective outlier detection algorithm for categorical data.

[BibT_eX]

[DOI]

Xingwang Zhao

Int. J. Mach. Learn. Cybern., 2014

Trend analysis of categorical data streams with a concept change method.

[BibT_eX]

[DOI]

Inf. Sci., 2014

2013

The Impact of Cluster Representatives on the Convergence of the (K)-Modes Type Clustering.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2013

A weighting k-modes algorithm for subspace clustering of categorical data.

[BibT_eX]

[DOI]

Neurocomputing, 2013

A novel fuzzy clustering algorithm with between-cluster information for categorical data.

[BibT_eX]

[DOI]

Fuzzy Sets Syst., 2013

A Concept-Drifting Detection Algorithm for Categorical Evolving Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2013

A cross cluster-based collaborative filtering method for recommendation.

[BibT_eX]

[DOI]

Ming Gao

Proceedings of the IEEE International Conference on Information and Automation, 2013

2012

The K -Means-Type Algorithms Versus Imbalanced Data Distributions.

[BibT_eX]

[DOI]

IEEE Trans. Fuzzy Syst., 2012

Determining the number of clusters using information entropy for mixed data.

[BibT_eX]

[DOI]

Pattern Recognit., 2012

A dissimilarity measure for the k-Modes clustering algorithm.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2012

A cluster centers initialization method for clustering categorical data.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2012

2011

A novel attribute weighting algorithm for clustering high-dimensional categorical data.

[BibT_eX]

[DOI]

Pattern Recognit., 2011

A data labeling method for clustering categorical data.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2011

2010

A Framework for Clustering Categorical Time-Evolving Data.

[BibT_eX]

[DOI]

IEEE Trans. Fuzzy Syst., 2010

2009

A new initialization method for categorical data clustering.

[BibT_eX]

[DOI]

Liang Bai

Expert Syst. Appl., 2009

An initialization method for the K-Means algorithm using neighborhood model.

[BibT_eX]

[DOI]