Big Data Analytics

Featured article: Evaluating associative classification algorithms for Big Data

Associative classification aims at building accurate and interpretable classifiers by means of association rules. A major problem in this field is that existing proposals do not scale well for Big Data. This work proposes adaptations of common associative classification algorithms for different Big Data platforms.

Amir Hussain, Editor-in-Chief

Amir Hussain obtained his BEng and PhD from the University of Strathclyde in Glasgow. Following a Research Fellowship at the University of Paisley and a research Lectureship at the University of Dundee, he joined the University of Stirling in 2000, where he is currently Professor of Computing Science and founding Director of the Cognitive Big Data Informatics (CogBID) Laboratory. His research interests are multi-disciplinary with a focus on brain-inspired, multi-modal cognitive big data technology for solving complex real-world problems. He has authored over 300 publications (including over a dozen books and over 100 journal papers), conducted and led collaborative research with industry, partnered in major European and international research programs, and supervised more than 30 PhDs. 

In addition to his role as Editor-in-Chief of Big Data Analytics, Amir is also founding Chief Editor of the Springer journal Cognitive Computation, and Associate Editor for a number of other leading journals. He has served as an invited speaker and organizing committee co-chair for over 50 top international conferences and workshops. He has served as the founding Publications Co-Chair of the INNS Big Data section and the annual INNS Conference on Big Data, and Chapter Chair of the IEEE UK and RI’s Industry Applications Society. He is a Fellow of the UK’s Higher Education Academy and Senior Fellow of the Brain Sciences Foundation.