Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

Outlier algorithms are becoming increasingly complex. Thereby, they become much less
interpretable to the data scientists applying the algorithms in real-life settings and to end …

Understanding the competitiveness of hotel brands is important for hotel managers to shape
their brands and initiate effective marketing strategies and business developments …

Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid
mining too many useless patterns. However, this method is difficult for users to set a suitable …

Outlier detection is an important task in many domains and is intensively studied in the past
decade. Further, how to explain outliers, ie, outlier interpretation, is more significant, which …

The growth of databases in the healthcare domain opens multiple doors for machine
learning and artificial intelligence technology. Many medical devices are available in the …

We address the problem of outlying aspects mining: given a query object and a reference
multidimensional data set, how can we discover what aspects (ie, subsets of features or …