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 …

Data quality management, especially data cleansing, has been extensively studied for many
years in the areas of data management and visual analytics. In the paper, we first review and …

In this paper, we develop a visual analysis method for interactively improving the quality of
labeled data, which is essential to the success of supervised and semi-supervised learning …

This textbook presents the main principles of visual analytics and describes techniques and
approaches that have proven their utility and can be readily reproduced. Special emphasis …

Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis.
Pre‐processing pipelines often require multiple routines to address data quality challenges …

The use of good-quality data to inform decision making is entirely dependent on robust
processes to ensure it is fit for purpose. Such processes vary between organisations, and …

Data quality management and assessment play a vital role for ensuring the trust in the data
and its fitness-of-use for subsequent analysis. The transformation history of a data wrangling …

摘要随着人工智能技术的突破性进展, 人工智能与可视化的交叉研究成为当前的研究热点之一,
为人工智能和大数据分析领域的若干核心难题提供了启发式的理论, 方法和技术. 一方面 …

Low quality automatic identification system (AIS) data often mislead analysts to a
misunderstanding of ship behavior analysis and to making incorrect navigation risk …

Ensuring data quality of household travel survey data is often tedious and, thus, time-
consuming. To speed up the process of data-checking and to gain an in-depth …