From basic data mining concepts to state-of-the-art advances, this book covers the theory of
the subject as well as its application in a variety of fields. It discusses the incorporation of …

Imbalanced time-series classification (ITSC) is ubiquitous in many real-world applications. In
this study, a novel cost-sensitive deep learning framework, namely ACS-ATCN, is proposed …

A novel method for automated annual layer counting in seasonally-resolved paleoclimate
records has been developed. It relies on algorithms from the statistical framework of hidden …

We introduce a framework for exploring and learning representations of log data generated
by enterprise-grade security devices with the goal of detecting advanced persistent threats …

Multivariate time series (MTS) data is an important class of temporal data objects and it can
be easily obtained. However, the MTS classification is a very difficult process because of the …

Continuous time series data often comprise or contain repeated motifs—patterns that have
similar shape, and yet exhibit nontrivial variability. Identifying these motifs, even in the …

Creating a human-robot interface is a daunting experience. Capabilities and functionalities
of the interface are dependent on the robustness of many different sensor and input …

This paper proposes a general probabilistic framework for shape-based modeling and
classification of waveform data. A segmental hidden Markov model (HMM) is used to …

Many real-world applications, such as those related to sensors, allow collecting large
amounts of inexpensive unlabeled sequential data. However, the use of supervised …