Time series segmentation (TSS) tries to partition a time series (TS) into semantically meaningful segments. It's an important unsupervised learning task applied to large, real-world sensor signals for ...

⚡️ 72× faster than PTv3 for end-to-end semantic segmentation ⚡️ 5.3x faster than SPT for end-to-end semantic segmentation Simply run install.sh to install all dependencies in a new conda environment ...

Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data—e.g., given a ...

CLARANS (Clustering Large Applications based on RANdomized Search) is a Data Mining algorithm designed to cluster spatial data. CLARANS is a clustering algorithm that focuses on spatial data mining, ...

Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network ...

Modern data analysis requires solving hard optimization problems with a large number of parameters and a large number of constraints. A successful approach is to replace these hard problems by ...

The International Conference on Theory and Applications of Satisfiability Testing (SAT) is the premier annual meeting for researchers focusing on the theory and applications of the propositional ...