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 …

Mining sequential data is an old topic that has been revived in the last decade, due to the
increasing availability of sequential datasets. Most works in this field are centred on the …

Time-Series clustering is one of the important concepts of data mining that is used to gain
insight into the mechanism that generate the time-series and predicting the future values of …

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 …

K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

Studying the pathways of ligand–receptor binding is essential to understand the mechanism
of target recognition by small molecules. The binding free energy and kinetics of protein …

An automatic stock market categorization system would be invaluable to individual investors
and financial experts, providing them with the opportunity to predict the stock price changes …

Time series averaging in dynamic time warping (DTW) spaces has been successfully
applied to improve pattern recognition systems. This article proposes and analyzes …

In the last decade, outlier detection for temporal data has received much attention from data
mining and machine learning communities. While other works have addressed this problem …