One or High Dimensional Time-Series Clustering – Methods Comparison, Benchmarks and New Features Insights
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 1873
Special Issue Editor
Interests: statistical signal processing; information geometry; topology; parametric subspace-based estimation; health-care systems; dynamical systems; complex networks
Special Issue Information
Dear Colleagues,
In many applications such as gene expression in biology, market analysis or portfolio building in finance, mechanics, telecommunication networks, speech processing, healthcare, the extraction and recognition of common patterns, the detection of anomalies, the definition and monitoring of processes, whether organizational or of production, can only be done by means of unsupervised clustering methods.
This Special Issue invites papers on the subject of clustering of one- or high-dimensional time series. We request papers incorporating new ideas, results, innovative and modern methodologies. Particular attention will be given to works exploiting the geometric or topological character of time series in their time, frequency, shape, statistical or any new representation. We also encourage submissions including new database benchmarks, comparison of methods, cluster number estimation or the definition of new performance evaluation criteria.
Prof. Dr. Guillaume Bouleux
Guest Editor
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Keywords
- shape space
- DTW
- distances
- manifolds
- topology data analysis
- networks
- graphs
- K-means
- HDSCAN
- K-nearest neighbors
- V-measure score
- Hopkins
- high-dimensional time series
- geodesics
- data set UCR
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