A clustering algorithm based on fuzzy sets and its application in learning analytics

In this paper, we developed a soft clustering algorithm, named NDFS clustering, based on fuzzy sets defined over a multi-dimensional feature space where each fuzzy set represents a cluster. The NDFS clustering algorithm implements an expectation–maximization (EM) paradigm to solve an optimisation problem with objective function described by the degrees of membership of data points with respect to fuzzy sets and with necessary constraints. Details of the NDFS clustering algorithm are discussed. The NDFS clustering algorithm is then applied to analyse primary school learning performance. The case study demonstrates the flexibility and performance of the NDFS clustering algorithm.