Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

Clustering non-Euclidean data is difficult, and one of the most used algorithms besides
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …

Today 3D models and point clouds are very popular being currently used in several fields,
shared through the internet and even accessed on mobile phones. Despite their broad …

Clustering algorithms have emerged as an alternative powerful meta-learning tool to
accurately analyze the massive volume of data generated by modern applications. In …

The growth of social media over the last decade has revolutionized the way individuals
interact and industries conduct business. Individuals produce data at an unprecedented rate …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

Anomaly detection is an important data analysis task. It is used to identify interesting and
emerging patterns, trends and anomalies from data. Anomaly detection is an important tool …