"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
The next industrial revolution is here. Whether you call it Industry 4.0 or Industrial IoT or Digital Transformation, the increased access to machine and operational data, proliferation of two-way communication, speed of data flow, combined with the lower cost of computing, connectivity and storage has created the perfect environment to transform industrial operations. The time series data generated by these operations, if harnessed, can provide actionable insights to reduce downtime as well as improve throughput, operator safety and product quality. McKinsey & Company predicts that the next 20 percent productivity rise in operations will come from digital analytics, and machine learning-enabled pattern recognition is playing a significant role in enhancing production operations. Time series data generated in discrete and process manufacturing operations is very rich in information that can provide insights on the current and future health of the production equipment and lines.
We consider a problem that involves finding similar elements in a collection of sets. The problem is motivated by applications in machine learning and pattern recognition. We formulate the similar elements problem as an optimization and give an efficient approximation algorithm that finds a solution within a factor of 2 of the optimal. The similar elements problem is a special case of the metric labeling problem and we also give an efficient 2-approximation algorithm for the metric labeling problem on complete graphs.
Since 9/11, border patrol agencies around the world have focused on improving their abilities to quickly assess threats from passengers and cargo entering the country. Based on its work with several countries on border protection, Unisys developed the LineSight software, which uses advanced analytics that assesses risk in near real time. Rather than relying solely on pattern recognition based on historical data, LineSight assesses risk from the initial intent to travel and refines that assessment as current information becomes available -- beginning with a traveler's visa application, reservation, ticket purchase, seat selection, check-in and arrival, the company said. The software provides similar risk assessments for cargo shipments based on manifest forms, customs declaration or airline bills. "It became clear more recently that statistical methods and analytical tools would be a better approach than trying to consolidate watch lists to find patterns."
These are all related, yet distinct, concepts that have been used for a very long time to describe an aspect of data mining that many would argue is the very essence of the term data mining: taking a set of data and applying statistical methods to find interesting and previously-unknown patterns within said set of data. We aren't looking to classify instances or perform instance clustering; we simply want to learn patterns of subsets which emerge within a dataset and across instances, which ones emerge frequently, which items are associated, and which items correlate with others. It's easy to see why the above terms become conflated. So, let's have a look at this essential aspect of data mining. Foregoing the Apriori algorithm for now, I will simply use the term frequent pattern mining to refer to the big tent of concepts outlined above, even if somewhat flawed (and even if I personally prefer the less often used term association mining).
This post would probably be the last in my series about merging R and ArcGIS. In August unfortunately I would have to work for real and I will not have time to play with R-Bridge any more. In this post I would like to present a toolbox to perform some introductory point pattern analysis in R through ArcGIS. Basically, I developed a toolbox to perform the tests I presented in my previous post about point pattern analysis. In there, you can find some theoretical concepts that you need to know to understand what this toolbox can do. I will start by introducing the sample dataset we are going to use, and then simply show the packages available.
Investment and interest in Artificial Intelligence (AI) is now white hot as we enter the second decade of the 21st century. Interestingly, AI is now almost a passé term. Machine Learning and advanced pattern recognition is now gaining notoriety as the "new" approach to apply the concept of AI, having been successfully applied to many problems in academia and industry.
Summary: Image processing is a rapidly evolving field with immense significance in science and engineering. One of the latest applications of Image processing is in Intelligent Character Recognition (ICR). Intelligent Character Recognition is the computer translation of handwritten text into machine-readable and machine-editable characters. It is an advanced version of Optical Character Recognition system that allows fonts and different styles of handwriting to be recognized during processing with high accuracy and speed. ICR, in combination with OCR and OMR (Optical Mark Recognition), is used in forms processing.
In this article, you will learn some modern techniques to detect whether a sequence appears as random or not, whether it satisfies the central limit theorem (CLT) or not -- and what the limiting distribution is if CLT does not apply -- as well as some tricks to detect abnormalities. Detecting lack of randomness is also referred to as signal versus noise detection, or pattern recognition.