Clustering has primarily been used as an analytical technique to group unlabeled data for
extracting meaningful information. The fact that no clustering algorithm can solve all …

An increase in the size of data repositories of spatiotemporal data has opened up new
challenges in the fields of spatiotemporal data analysis and data mining. Foremost among …

There is a growing trend of cyberattacks against Internet of Things (IoT) devices; moreover,
the sophistication and motivation of those attacks is increasing. The vast scale of IoT, diverse …

Since the early 2010s, the meal delivery business went through a veritable revolution due to
online food delivery platforms. By allowing customers to quickly order from a wide range of …

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper
use (and understanding) of machine learning methods in practical applications becomes …

Driving style may have an important effect on traffic safety. Proactive crash risk prediction for
lane-changing behaviors incorporating individual driving styles can help drivers make safe …

Mixed data comprises both numeric and categorical features, and mixed datasets occur
frequently in many domains, such as health, finance, and marketing. Clustering is often …

With the rapid development and popularization of Internet and mobile communication
technologies, text data mining has attracted much attention. In particular, with the wide use …

Telecom Companies logs customer's actions which generate a huge amount of data that can
bring important findings related to customer's behavior and needs. The main characteristics …

Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of
sentiment and emotion, as well as physiological-emotion and emotion-based stress …