Unsupervised Learning

Abstract

This article presents a review of traditional and current methods of classification in the framework of unsupervised learning. Focus is placed on cluster analysis and self‐organizing neural networks: two vector quantization methods aiming at minimizing the distance between an input vector and its representation. The learning is unsupervised as no predefined cluster structure of the input data is assumed. The review of cluster analysis methods covers (i) hard clustering, hierarchical and nonhierarchical, whose aim is to assign exact units (objects) to clusters (i.e., with membership degree equal to 1); (ii) fuzzy clustering, where the membership degree of a unit to a cluster is in the range [0; 1]; and (iii) mixture clustering, a model‐based clustering consisting in fitting a mixture model to data and identifying each cluster with one of its components. These clustering methods are reviewed in all the variants related to the presence of complex and/or big data structures and to the presence of outliers. The self‐organizing maps are also presented as artificial neural network, the cells (neurons) of which become specifically tuned to various input data patterns or classes of patterns through an unsupervised learning process.

Analysis of a paediatric cohort of dyslipidaemic patients using unsupervised learning methods provides insights into the biochemical phenotypes of familial hypercholesterolemia

“…The method used in this work, hierarchical clustering, organises data into a hierarchical structure based on appropriate (dis)similarity or distance measures between every pair of subjects in the dataset. In the case of agglomerative nesting, the clustering algorithm groups data by means of a sequence of partitions starting with each unit (subject) forming a separate cluster and then merging similar clusters into larger clusters [13]–[15]. The results of these approaches can thus support a better understanding of the degree of heterogeneity that can be found in a given population/dataset.…”