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.
no code implementations • 17 May 2019 • Zengyou He, Guangyao Xu, Chaohua Sheng, Bo Xu, Quan Zou
By utilizing this framework as a tool, we propose new sequence classification algorithms that are quite different from existing solutions.
no code implementations • 3 Jan 2019 • Zengyou He, Chaohua Sheng, Yan Liu, Quan Zou
After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value.
1 code implementation • Advances in Knowledge Discovery and Data Mining 2006 • Zengyou He, Shengchun Deng, Xiaofei Xu, Joshua Zhexue Huang
The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data.