Welcome to the autumn edition of Informer! For those of you who don’t know, Informer is the newsroom of the Information Retrieval Specialist Group (IRSG) of the British Computer Society. We spare no effort in getting the hottest pieces of (IRSG) news, getting the best contributors and compiling the most up-to-date list of IR-related events. What else would anybody want? Let’s jump straight in and see where ECIR 2016 is going to be hosted.
By Tony Russell-Rose on November 3, 2014
In a previous post I discussed some initial investigations into the use of unsupervised learning techniques (i.e. clustering) to identify usage patterns in web search logs. As you may recall, we had some initial success in finding interesting patterns of user behaviour in the AOL log, but when we tried to extend this and replicate a previous study of the Excite log, things started to go somewhat awry. In this post, we investigate these issues, present the results of a revised procedure, and reflect on what they tell us about searcher behaviour.
By Roman Kern on November 3, 2014
The Know-Center is Austria’s leading research centre for data-driven business and big data analytics, with the purpose to bridge the gap between industry and academia. Therefore, the state-of-the-art in science is applied to problem setting within the industry to stir innovation, and to actively research in areas not being covered currently by the research community.
By Markus Schedl on November 3, 2014
The Department of Computational Perception at the Johannes Kepler University Linz was founded in October 2004, with the appointment of Prof. Gerhard Widmer. Its mission is to develop computational models and algorithms that permit computers to perceive and “understand” aspects of the external world, where we interpret “perception” in the widest sense of the word, as the extraction of useful high-level information from complex, possibly low-level data, including text, audio, video, image, and sensor data.
By Song Chen on November 3, 2014
Graph-Based Clustering and Data Visualization Algorithms: A review by Song Chen
ISBN: 978-1-4471-5157-9 (Print) – 978-1-4471-5158-6 (Online)
The book, authored by Ágnes Vathy-Fogarassy and Janos Abonyi presents the topic of graph-based clustering and presents several algorithms. Besides introducing several related methods in representing and clustering a network, the authors also proposed a novel clustering algorithm to cluster and visualize the datasets. The first part of the book talks about vector quantization methods and different ways to represent graphs with vectors and to reduce the dimensionalities. The second part of this book introduces a few clustering algorithms including Neighborhood Based algorithm, Minimal Spanning Tree Clustering and Jarvis-Patrick Clustering algorithms. The last part of this book deals with high dimensional data and introduces a few ways to reduce the dimensionalities.
By Andy Macfarlane on November 3, 2014
One day events
Search Solutions 2014. Our annual one day practioner focused event, with a tutorial day. 26, 27 November 2014. http://irsg.bcs.org/SearchSolutions/2014/sse2014.php