Abstract: Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, ...

Abstract: This paper presents a new method that combines deep k-means clustering with granule mining approaches to utilise contextual information for improving outlier detection and classification.