Implementing Clustering and Classification Approaches for Big Data with MATLAB

Data sets grow rapidly, driven by increasing storage capacities as well as by the wish to equip more and more devices with sensors and connectivity. In mechanical engineering Big Data offers new possibilities to gain knowledge from existing data for product design, manufacturing, maintenance and failure prevention. Typical interests when analyzing Big Data are the identification of clusters, the assignment to classes or the development of regression models for prediction. This paper assesses various Big Data approaches and chooses adequate clustering and classification solutions for a data set of simulated jet engine signals and life spans. These solutions include k-means clustering, linear discriminant analysis and neural networks. MATLAB is chosen as the programming environment for implementation because of its dissemination in engineering disciplines. The suitability of MATLAB as a tool for Big Data analysis is to be evaluated. The results of all applied clustering and classification approaches are discussed and prospects for further adaption and transferability to other scenarios are pointed out

Headache for ophthalmologists: current advances in headache understanding and management.

Patients with headache and head pain are often referred to ophthalmologists. These symptoms can either be associated with underlying ophthalmic conditions, or more often are headache disorders unrelated to the eyes. Understanding the phenotype of the headache is critical for advice, safe discharge or onward referral. This review will provide an update on the criteria for common headache disorders that are often seen by ophthalmology and embrace disorders associated with ophthalmic diseases. It will also describe the changing management of migraine and outline recent therapies that are currently available