Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

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 …

Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …

Time series clustering has been shown effective in providing useful information in various
domains. There seems to be an increased interest in time series clustering as part of the …

Clustering is a powerful vehicle to reveal and visualize structure of data. When dealing with
time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the …

Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …

The airline industry is moving toward proactive risk management, which aims to identify and
mitigate risks before accidents occur. However, existing methods for such efforts are limited …

Multivariate time series (MTS) datasets broadly exist in numerous fields, including health
care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot …

In this paper, we propose a technique for time series clustering using community detection in
complex networks. Firstly, we present a method to transform a set of time series into a …

Listener head generation centers on generating non-verbal behaviors (eg, smile) of a
listener in reference to the information delivered by a speaker. A significant challenge when …