Article
Biochemical Research Methods
Aimin Li, Siqi Xiong, Junhuai Li, Saurav Mallik, Yajun Liu, Rong Fei, Hongfang Zhou, Guangming Liu
Summary: In this study, a novel clustering method AngClust based on angular features was proposed for short-term gene expression. AngClust measures the change of trend in gene expression levels at neighboring time points to determine the similarity of expression trends among different genes. The effectiveness of AngClust was demonstrated to outperform two other clustering measures on yeast gene expression dataset.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Guoliang He, Wenjun Jiang, Rong Peng, Ming Yin, Min Han
Summary: In this study, a variable-weighted K-medoids clustering algorithm is proposed to address the issues of correlations and redundancies between variables in MTS data. In addition, a new approach is introduced to handle the initialization sensitivity problem, along with an ensemble clustering framework based on density peaks to further enhance the clustering performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tuanfei Zhu, Cheng Luo, Zhihong Zhang, Jing Li, Siqi Ren, Yifu Zeng
Summary: This paper introduces a structure-preserving Oversampling method for high-dimensional imbalanced time series classification, OHIT, and integrates it into boosting framework to form a new ensemble algorithm OHITBoost. Extensive experiments on several publicly available time-series datasets demonstrate their effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Kalpathy Jayanth Krishnan, Kishalay Mitra
Summary: This study proposes a modified Self Organizing Map algorithm for clustering time series data. By modifying the original steps of the algorithm and using specific initialization methods and similarity measures, this algorithm outperforms other popular clustering algorithms in terms of clustering performance and computation time.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Civil
Georgia Papacharalampous, Hristos Tyralis, Yannis Markonis, Martin Hanel
Summary: In this study, a new methodological framework is proposed for exploring and comparing multi-scale analyses in a hydroclimatic context, in order to comprehensively understand the behaviors of geophysical processes and evaluate time series simulation models. By computing the feature values at different temporal resolutions and three hydroclimatic time series types, similarities and differences in the evolution patterns are identified. The computed features are also used for meaningful clustering of hydroclimatic time series, which allows for interpretation of hydroclimatic similarity at various temporal resolutions.
JOURNAL OF HYDROLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Jiangyong Duan, Lili Guo
Summary: This paper proposes an optimization framework for adaptively estimating the lengths and representations of different patterns in subsequence clustering. By minimizing the errors in subsequence clustering and segmentation under time series cover constraint, our framework can automatically extract unknown variable-length subsequence clusters in time series.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Theory & Methods
Ling Wang, Peipei Xu, Qian Ma
Summary: Clustering is a popular data mining method for analyzing time series, and the incremental fuzzy clustering algorithm (IFCTS) proposed in this paper shows good clustering accuracy and efficiency for both equal-length and unequal-length time series.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Iago Vazquez, Jose R. Villar, Javier Sedano, Enrique de la Cal, Svetlana Simic
Summary: This study proposes an ensemble of MTS clustering methods that merges different MTS representations and distance functions, which helps clustering practitioners in selecting suitable prototypes. The results show bias towards the best methods and emphasize the importance of metrics in guiding the clustering process. Further work involves the study of digital markers to compare MTS representations and distance functions, as well as using external metrics to balance the aggregation of methods.
Article
Computer Science, Artificial Intelligence
Dino Ienco, Roberto Interdonato
Summary: A huge amount of data from sensors can be organized as multivariate time series. In a limited background knowledge setting, semi-supervised clustering methods can effectively utilize a small amount of knowledge. We propose a constrained deep embedding time series clustering framework that manages the temporal dimension and exploits Must-link and Cannot-link constraints for better clustering results.
Review
Computer Science, Information Systems
Ali Alqahtani, Mohammed Ali, Xianghua Xie, Mark W. Jones
Summary: The article presents a detailed review of time-series data analysis, focusing on deep time-series clustering (DTSC) and a case study in movement behavior clustering using the deep clustering method. It discusses modifications made to DCAE architectures for time-series data and reviews recent works on deep clustering of time-series data. The article also identifies state-of-the-art developments in this field and offers an outlook on DTSC from five important perspectives.
Article
Automation & Control Systems
Hongyue Guo, Lidong Wang, Xiaodong Liu, Witold Pedrycz
Summary: This article introduces a two-stage time-series clustering approach, which involves dimensionality reduction based on information granules and fuzzy clustering using dynamic time warping and fuzzy C-means algorithm. Experiments on UCR time-series database and Chinese stocks datasets validate the effectiveness and advantages of the proposed fuzzy clustering approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Information Systems
Yucheng Li, Derong Shen, Tiezheng Nie, Yue Kou
Summary: This paper discusses the research on time series clustering and the importance of shape-based clustering algorithms. It proposes a new algorithm called FrOKShape, which shows excellent results when combined with traditional clustering algorithms and comparable performance to existing algorithms.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xiaosheng Li, Jessica Lin, Liang Zhao
Summary: With the increasing availability of time series data, data mining methods need to have low time complexity to handle large and fast-changing data. This study introduces a novel time series clustering algorithm with linear time complexity, which outperforms other methods in terms of accuracy.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Computer Science, Artificial Intelligence
Nevin Guler Dincer, Arzu Ekici
Summary: This study introduces a new Fuzzy Time Series approach, DPFTS, which shows good performance in handling time series data with multiple cross-sections and has strong predictive capabilities.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Computer Science, Artificial Intelligence
Xiyang Yang, Fusheng Yu, Witold Pedrycz, Zhiwei Li
Summary: This paper proposes a trend-oriented time series granulation method to transform a long numerical time series into a relatively short granular time series. The transformed granular time series captures the main characteristics of the original time series and saves calculation in time series clustering. The distance measures for unequal-size LFIGs and LFIG time series are defined, and the k-medoids method is employed to cluster datasets from UCR time-series database.
APPLIED SOFT COMPUTING
(2023)
