Towards Real-Time Feature Tracking Technique using Adaptive Micro-Clusters

Authors: Mahmood Shakir Hammoodi, Frederic Stahl, Mark Tennant, Atta Badii Abstract: Data streams are unbounded, sequential data instances that are generated with high velocity. Classifying sequential data instances is a very challenging problem in machine learning with applications in network intrusion detection, financial markets and sensor networks. Data stream classification is concerned with the automatic […]

On expressiveness and uncertainty awareness in rule-based classification for data streams

Authors: Thien Le, Frederic Stahl, Mohamed Medhat Gaber, João Bártolo Gomes, Giuseppe Di Fatta Abstract: Mining data streams is a core element of Big Data Analytics. It represents the velocity of large datasets, which is one of the four aspects of Big Data, the other three being volume, variety and veracity. As data streams in, models are constructed using data […]

Scalable real-time classification of data streams with concept drift

Authors: Mark Tennant, Frederic Stahl, Omer Rana, João Bártolo Gomes Abstract: Inducing adaptive predictive models in real-time from high throughput data streams is one of the most challenging areas of Big Data Analytics. The fact that data streams may contain concept drifts (changes of the pattern encoded in the stream over time) and are unbounded, imposes […]

A Text Mining Framework for Big Data

  Authors: Niki Pavlopoulou, Aeham Abushwashi, Frederic Stahl and Vittorio Scibetta Abstract: Text Mining is the ability to generate knowledge (insight) from text. This is a challenging task, especially when the target text databases are very large. Big Data has attracted much attention lately, both from academia and industry. A number of distributed databases, search […]

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 […]