A Statistical Learning Method to Fast Generalised Rule Induction Directly from Raw Measurements

Authors: Thien Le, Frederic Stahl, Chris Wrench and Mohamed Gaber Abstract: Induction of descriptive models is one of the most important technologies in data mining. The expressiveness of descriptive models are of paramount importance in applications that examine the causality of relationships between variables. Most of the work on descriptive models has concentrated on less […]

new PhD project: Developing a Data Mining Method for Data Diffusion Detection

Big Data Analytics refers to the analytics/data mining of large and complex datasets. Special efficient algorithms are needed to process and analyse Big Data. This project is mainly concerned with the development of an analytics methodology for diffusion detection of spatial-temporal data. Loosely speaking, it is about the development of a method that enables the […]

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: 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 […]

PhD Project in “Real-time Parallel Big Data Analysis”

Description The Real-time Parallel Big Data Analysis project is concerned with the development of scalable Big Data Analytics techniques for fast data streams. There is significant activity going on in Big Data Analytics throughout the University, as Big Data techniques underpin large areas of the University’s research activity in much the same way that the […]

PhD Project in”Automatic Classification of Data Streams with Sparse Class Labels”

Description Data Stream Mining has become a hot topic and is concerned with the analytics of data that arrives in real-time and at a fast speed. Two general challenges in Data Stream Mining are (1) the data stream is infinite and storing the data and learning off line is not possible and (2) the pattern […]

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 […]