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20082026
most citedClustering-based Anomaly Detection in Multivariate Time Series Data

230 citations · 402 across the 17 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Evidential Information Fusion on Possibilistic Structure

Qianli Zhou, Ye Cui, Zhen Li +2

Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restric…

cs.AI2025230 cited

Clustering-based Anomaly Detection in Multivariate Time Series Data

Jinbo Li, Hesam Izakian, Witold Pedrycz +1

Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes…

cs.AI2025144 cited

Multivariate Time series Anomaly Detection:A Framework of Hidden Markov Models

Jinbo Li, Witold Pedrycz, Iqbal Jamal

In this study, we develop an approach to multivariate time series anomaly detection focused on the transformation of multivariate time series to univariate time series. Several tra…

cs.AI20216 cited

A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems

Yongming He, Guohua Wu, Yingwu Chen +1

There hardly exists a general solver that is efficient for scheduling problems due to their diversity and complexity. In this study, we develop a two-stage framework, in which rein…

cs.AI2020

Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector

Kaijie Xu, Witold Pedrycz, Zhiwu Li

Information granules have been considered to be the fundamental constructs of Granular Computing (GrC). As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is one of t…

cs.AI2018

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Mahardhika Pratama, Witold Pedrycz, Geoffrey I. Webb

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizin…