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Applying Machine Learning to Text Mining with Amazon S3 and RapidMiner

@machinelearnbot

By some estimates, 80% of an organization's data is unstructured content. This content includes web pages, call center transcripts, surveys, feedback forms, legal documents, forums, social media, and blog articles. Therefore, organizations must analyze not just transactional information but also textual content to gain insight and boost performance. A powerful way to analyze this textual content is by using text mining. Text mining typically applies machine learning techniques such as clustering, classification, association rules and predictive modeling.


Applying Machine Learning to Text Mining with Amazon S3 and RapidMiner

#artificialintelligence

By some estimates, 80% of an organization's data is unstructured content. This content includes web pages, call center transcripts, surveys, feedback forms, legal documents, forums, social media, and blog articles. Therefore, organizations must analyze not just transactional information but also textual content to gain insight and boost performance. A powerful way to analyze this textual content is by using text mining. Text mining typically applies machine learning techniques such as clustering, classification, association rules and predictive modeling.


Global Big Data Conference

#artificialintelligence

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible. The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually. For forensic accounting investigators, unearthing these crimes manually is an uphill climb.


Business must tone down its lust for big data

ZDNet

It should come as no surprise that when key industry bodies write submissions to government consultations they're self-serving. That's what such lobby groups are for, right?


From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability

arXiv.org Artificial Intelligence

Advances in Data Science are lately permeating every field of Transportation Science and Engineering, making it straightforward to imagine that developments in the transportation sector will be data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a "story" intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed to software running on automatic devices, actuators or control systems producing, in turn, complex information flows between users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. The present work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded on this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the everchanging phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within the Data Science realm that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.