Three-dimensional data are increasingly prevalent across biomedical and social domains.
Notable examples are gene-sample-time, individual-feature-time, or node-node-time data …

Earthquake prediction has been a challenging research area, where a future occurrence of
the devastating catastrophe is predicted. In this work, sixty seismic features are computed …

The analysis of temporal data has gained increasing attention in recent years, aiming to
identify patterns and trends that change over time. Temporal triclustering is a promising …

Feature selection algorithm contributes a lot in the domain of medical diagnosis. Choosing a
small subset of genes that enable a classifier to predict the presence or type of disease …

Precision agriculture focuses on the development of site-specific harvest considering the
variability of each crop area. Vegetation indices allow the study and delineation of different …

The prediction of the earthquake has been a testing investigation field, where a prediction of
the impending incidence of destructive calamity is made. In this research, eight seismic …

Triclustering techniques are applied to analyze three-dimensional gene expression
microarray data to retrieve group of genes under the tested samples over certain time points …

Seismogenic source zone models, including the delineation and the characterization, still
have a role to play in seismic hazard calculations, particularly in regions with moderate or …

Agriculture has undergone some very important changes over the last few decades. The
emergence and evolution of precision agriculture has allowed to move from the uniform site …

Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper, a
new triclustering approach for data streams is introduced. It follows a streaming scheme of …