Abstract Machine learning provides researchers a unique opportunity to make metabolic
engineering more predictable. In this review, we offer an introduction to this discipline in …

In the last 5 years there have been a large number of new time series classification
algorithms proposed in the literature. These algorithms have been evaluated on subsets of …

This paper presents an empirical study of feature extraction methods for the application of
low-speed slew bearing condition monitoring. The aim of the study is to find the proper …

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

A highly comparative, feature-based approach to time series classification is introduced that
uses an extensive database of algorithms to extract thousands of interpretable features from …

The first edition of this book appeared in 1991 when the technology was new and there were
not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker …

Wounds especially chronic ones significantly affect the quality of patients' life and present a
severe financial burden for the healthcare industry. Timely and effective management of …

We investigate how random projection can best be used for clustering high dimensional
data. Random projection has been shown to have promising theoretical properties. In …

In recent years, the smart grid has attracted wide attention from around the world. Large
scale data are collected by sensors and measurement devices in a smart grid. Smart meters …

A time series is “a sequence X=(X1, X2, xm) of observed data over time", where m is the
number of observations. Tracking the behavior of a specific phenomenon/data in time can …