Clustering is a process widely studied in the field of pattern recognition. Despite the
existence of numerous algorithms and continuous innovation, there are still unresolved …

Time series clustering is a central machine learning task with applications in many fields.
While the majority of the methods focus on real-valued time series, very few works consider …

Hierarchical categorical data is commonly encountered in social science, genetics, and
other fields. The interactions between variables in hierarchical structures introduce …

This paper proposes a fuzzy C-medoids-based clustering method with entropy
regularization to solve the issue of grouping complex data as interval-valued time series …

Time series clustering is an important data mining task with a wide variety of applications.
While most methods focus on time series taking values on the real line, very few works …

Time series clustering is an essential machine learning task with applications in many
disciplines. While the majority of the methods focus on time series taking values on the real …

Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals
with real-valued time series, categorical time series have received much less attention …

Time series clustering is a central machine learning task with applications in many fields.
While the majority of the methods focus on real-valued time series, very few works consider …

The problem of testing the equality of the generating processes of two categorical time
series is addressed in this work. To this aim, we propose three tests relying on a dissimilarity …

River water quality could be determined by understanding the capacity of pollutants in a
water body. Fuzzy C-Means (FCM) is one of the fuzzy clustering methods for determining …