Fuzzy Sets; Theory Statistical Applications of Fuzzy Sets; Multivariate Statistical Analysis; Latent Variables Models; Statistics of Extremes; Bayesian Statistics.
He currently teaches the Mathematical Analysis curricular unit in the IGE course, and the curricular unit of Systems and Operations Simulation, in the Master in Service Management and Technology.
research project at dinâmia'cet-IUL
The matrix factorization approach to fuzzy clustering can be looked at from several perspectives. The potential areas of research include among others: new approaches to prototypes, pattern recognition, computer vision, big data, cluster validation indices, estimation algorithms, software tools, and applications to any field of investigation where the fuzzy sets can be relevant. At the present time, we are essentially focusing on new approaches to prototypes, the cluster validation problem and to provide software for practitioners.
Suleman, A. (2017). A fuzzy clustering approach to evaluate individual competencies from REFLEX data, Journal of Applied Statistics 44(14), 2513—2533.
Suleman, A. (2017). Assessing a fuzzy extension of Rand index and related measures, IEEE Transactions on Fuzzy Systems 25(1), 237—244.
Suleman, A. (2015). A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering, Fuzzy Sets and Systems 270, 90–110.
Suleman, A., and Suleman, F. (2012). Ranking by competence using a fuzzy approach, Quality & Quantity, 46, 323–339.