Redescription mining is a field of knowledge discovery that aims at finding different descriptions of similar subsets of instances in the data. These descriptions are represented as rules inferred from one or more disjoint sets of attributes, called views. As such, they support knowledge discovery process and help domain experts in formulating new hypotheses or constructing new knowledge bases and decision support systems. In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences. Construction of large and heterogeneous redescription set relies on CLUS-RM algorithm and a novel, conjunctive refinement procedure that facilitates generation of larger and more accurate redescription sets. The work also introduces the variability of redescription accuracy when missing values are present in the data, which significantly extends applicability of the method. Crucial part of the framework is the redescription set extraction based on heuristic multi-objective optimization procedure that allows user to define importance levels towards one or more redescription quality criteria. We provide both theoretical and empirical comparison of the novel framework against current state of the art redescription mining algorithms and show that it represents more efficient and versatile approach for mining redescriptions from data.

EESTech Challenge is an international project that aims to gather machine learning enthusiast from all over the Europe.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

This year's news about what artificial intelligence (AI) can do in the arts has been both exciting and scary. Neural networks have learnt to paint like masters and compose sophisticated music. Those of us in creative endeavours might be as endangered by technological advances as blue-collar workers are often said to be - though we are protected by certain limitations that technology is never likely to overcome. Last summer, a team of Russian developers released Prisma, a mobile app based on the work of some German AI researchers. The neural network behind it could redraw an image using techniques it had learnt from studying the oeuvre of a number of painters, including Vincent Van Gogh and Edvard Munch. The end product was impressive: Prisma could reproduce brushstrokes and palettes, using only a photo for guidance, almost the way a human painter could have.

This year's news about what artificial intelligence can do in the arts has been both exciting and scary. Neural networks have learned to paint like masters and compose sophisticated music. Those of us in creative endeavors might be as endangered by technological advances as blue-collar workers are often said to be--though we are protected by certain limitations that technology is never likely to overcome. Last summer, a team of Russian developers released Prisma, a mobile app based on the work of some German artificial intelligence researchers. The neural network behind it could redraw an image using techniques it had learned from studying the oeuvre of a number of painters, including Vincent Van Gogh and Edvard Munch. The end product was impressive: Prisma could reproduce brushstrokes and palettes, using only a photo for guidance, almost the way a human painter could have.

A change in political leadership with Donald Trump's presidential victory and GOP control of Congress has raised expectation of policy shifts that could affect the regulatory compliance process. The incoming administration is promising to work to "dismantle the Dodd-Frank Act and replace it with new policies to encourage economic growth and job creation." This scenario would have plusses and minuses. On one hand, bank stocks are on the rise because of Trump's promise to lessen regulation. On the other hand, a complete dismantling of Dodd-Frank would mean that banks would have to overhaul the compliance processes that they have spent billions of dollars to put in place over the past six years.

Humanoid robot bartender "Carl" prepares a drink for a guest at the Robots Bar and Lounge in the eastern German town of Ilmenau. NEXT time you stop for petrol at a self-serve pump, say hello to the robot in front of you. Its life story can tell you a lot about the robot economy roaring toward us like an EF5 tornado on the prairie. Yeah, your automated petrol pump killed a lot of jobs over the years, but its biography might give you hope that the coming wave of automation driven by artificial intelligence (AI) will turn out better for almost all of us than a lot of people seem to think. The first crude version of an automated petrol-delivering robot appeared in 1964 at a station in Westminster, Colorado.

My colleague Amy Nicholson is the UK expert on Azure Machine Learning, the following blog post is after a quizzing session to get understand how to get started with Azure Machine Learning" Each student receives $100 of Azure credit per month, for 6 months. The Faculty member receives $250 per month, for 12 months. The Azure machine learning team provided a very nice walkthrough tutorial which covers a lot of the basics. This tutorial is really useful as it takes you through the entire process of creating an AzureML workspace, uploading data, creating an experiment to predict someone's credit risk, building, training, and evaluating the models, publishing your best model as a web service, and calling that web service. Now you need to learn how to import a data set into Azure Machine Learning, and where to find interesting data to build something amazing.

A flight over farmlands could be part of a future Uber ride. Recently there's been a lot of ink and pixels declaring 2016 the year of humanity's discontent. That's tough to deny in the realm of geopolitics, between awful conflicts in places like Syria, acts of terror worldwide and contentious elections in the UK and US. But the world went on, and so did important work in science and innovation. If you sweep the ugly parts of 2016 under the rug and then check the place out, it's not too shabby.

'Passengers' stars chat about future space travel, tech Sitting down with Chris Pratt, Jennifer Lawrence and Michael Sheen to talk about space travel and the tech in the film. The ramifications of waking people up from cryogenic stasis sooner than planned. "Passengers" has some lofty, fascinating concepts, but unfortunately, it never really connects with the core of any of them. It's a place he describes as relatively colonial; he's hoping for the chance to start over and become a useful member of society again by building things. Jim (Chris Pratt) and Aurora (Jennifer Lawrence): Now that I've woken you up, will you love me?