no code implementations • 4 May 2024 • Mudi Jiang, Lianyu Hu, Zengyou He, Zhikui Chen
Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy.
no code implementations • 16 Oct 2023 • Junjie Dong, Mudi Jiang, Lianyu Hu, Zengyou He
Existing pattern-based methods measure the discriminative power of each feature individually during the mining process, leading to the result of missing some combinations of features with discriminative power.
1 code implementation • 3 Sep 2023 • Junjie Dong, Xinyi Yang, Mudi Jiang, Lianyu Hu, Zengyou He
Categorical sequence clustering plays a crucial role in various fields, but the lack of interpretability in cluster assignments poses significant challenges.
1 code implementation • 14 Jul 2023 • Lianyu Hu, Junjie Dong, Mudi Jiang, Yan Liu, Zengyou He
The objective of clusterability evaluation is to check whether a clustering structure exists within the data set.
no code implementations • 6 Feb 2023 • Zengyou He, Yifan Tang, Lianyu Hu, Mudi Jiang, Yan Liu
In addition to the problem formulation on this new issue, we present a greedy algorithm called PIC (Personalized Interpretable Classifier) to identify a personalized rule for each individual test sample.
1 code implementation • 8 Nov 2022 • Lianyu Hu, Mudi Jiang, Yan Liu, Zengyou He
As a by-product, we can further calculate an empirical $p$-value to assess the statistical significance of a set of clusters and develop an improved gap statistic for estimating the cluster number.