Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

With the rapid development of biotechnology, the number of biological sequences has
grown exponentially. The continuous expansion of biological sequence data promotes the …

We present a comprehensive, detailed review of time-series data analysis, with emphasis on
deep time-series clustering (DTSC), and a case study in the context of movement behavior …

Event sequence data record series of discrete events in the time order of occurrence. They
are commonly observed in a variety of applications ranging from electronic health records to …

Deep Q-Network (DQN), as one type of deep reinforcement learning model, targets to train
an intelligent agent that acquires optimal actions while interacting with an environment. The …

In smart manufacturing, people are facing an increasing amount of industrial data derived
from various digitalized and connected sources in all kinds of formats. Analyzing and …

Interactive machine learning (IML) is an iterative learning process that tightly couples a
human with a machine learner, which is widely used by researchers and practitioners to …

Event sequence data is increasingly available. Many business operations are supported by
information systems that record transactions, events, state changes, message exchanges …

Visual analytics for time series data has received a considerable amount of attention.
Different approaches have been developed to understand the characteristics of the data and …

Event sequence mining is often used to summarize patterns from hundreds of sequences
but faces special challenges when handling racket sports data. In racket sports (eg, tennis …