Tag Archive | Data Mining

A rule dynamics approach to event detection in Twitter with its application to sports and politics

This paper has been published in Elsevier’s Expert Systems with Applications.  The manuscript is accessible online at: https://www.researchgate.net/profile/Mohamed_Gaber16/publication/295217245_A_Rule_Dynamics_Approach_to_Event_Detection_in_Twitter_with_Its_Application_to_Sports_and_Politics/links/56db2e8408aebe4638beebda.pdf?inViewer=0&pdfJsDownload=0&origin=publication_detail Authors: Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Carlos Martin Dancausa, Frederic Stahl and João Bartolo Gomes Abstract The increasing popularity of Twitter as social network tool for opinion expression as well as information retrieval has resulted in the […]

A method of rule induction for predicting and describing future alarms in a telecommunication network

Authors: Chris Wrench , Frederic Stahl, Thien Le, Giuseppe Di Fatta, Vidhyalakshmi Karthikeyan, Detlef Nauck Abstract: In order to gain insights into events and issues that may cause alarms in parts of IP networks, intelligent methods that capture and express causal relationships are needed. Methods that are predictive and descriptive are rare and those that do predict are often limited to using a […]

Towards Expressive Modular Rule Induction for Numerical Attributes

Authors: Manal Almutairi, Frederic Stahl, ,Matthew Jennings, Thien Le and Max Bramer Abstract The Prism family is an alternative set of predictive data mining algorithms to the more established decision tree data mining algorithms. Prism classifiers are more expressive and user friendly compared with decision trees and achieve a similar accuracy compared with that of […]

Towards cost-sensitive adaptation: When is it worth updating your predictive model?

This paper has been published in Elsevier’s Neurocomputing.  The manuscript is accessible online at: http://centaur.reading.ac.uk/38834/1/NEUCOM-D-13-01447R2.pdf Authors: Indre Zliobaite, Marcin Budka and Frederic Stahl Abstract Our digital universe is rapidly expanding, more and more daily activities are digitally recorded, data arrives in streams, it needs to be analyzed in real time and may evolve over time. […]

A Scalable Expressive Ensemble Learning Using Random Prism: A MapReduce Approach

This paper has been published in Springer’s Transactions on large-scale data and knowledge-centered systems.  The manuscript is accessible online at: http://centaur.reading.ac.uk/39793/1/typeinst.pdf Authors: Frederic Stahl, David May, Hugo Mills, Max Bramer and Mohamed Medhat Gaber Abstract The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science […]

A Survey of Data Mining Techniques for Social Network Analysis

This paper has been published in Episciences’s Journal of Data Mining and Digital Humanities.  The manuscript is accessible online at: http://centaur.reading.ac.uk/40754/1/AuthorFinal.pdf Authors: Mariam Adedoyin-Olowe, Mohamed Medhat Gaber and Frederic Stahl Abstract Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet […]

Random Prism: a noise‐tolerant alternative to Random Forests

This paper has been published in Wiley’s Expert Systems.  The manuscript is accessible online at: http://centaur.reading.ac.uk/32914/1/AI2011%20%281%29.pdf Authors: Frederic Stahl and Max Bramer Abstract Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for […]