The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

In this paper, a novel trajectory clustering algorithm-TAD-is proposed to extract trajectory
Stays based on spatial-temporal density analysis of data. Two new metrics-NMAST …

To enable Small and Medium-sized Enterprises (SMEs) level garment manufacturers to
measure and monitor the work of individual workers without incurring a large financial …

The evolution of IoT has increased the popularity of all types of sensing devices in a variety
of industrial fields and has resulted in enormous growth in the volume of sensor data …

This paper presents a new weighted local outlier factor method for anomaly detection, which
is underpinned with three novel components:(1) a piecewise linear representation defined …

Abstract Piecewise Linear Approximation (PLA) is an effective method used to represent and
compress a time series. It divides a time series into a number of segments, each of which is …

Time series analytics is a fundamental prerequisite for decision-making as well as
automation and occurs in several applications such as energy load control, weather …

Piecewise Linear Approximation (PLA) is one of the most widely used approaches for
representing a time series with a set of approximated line segments. With this compressed …

A manufacturing system collects big and heterogeneous data for tasks such as product
quality modeling and data-driven decision-making. However, as the size of data grows …

Data visualization is the primary means by which data analysts explore patterns, trends, and
insights in their data. Despite of their growing popularity, existing visualization tools (eg …