Automatic cluster labeling through Artificial Neural Networks

The clustering problem has been considered as one of the most important problems among those existing in the research area of unsupervised learning (a Machine Learning subarea). Although the development and improvement of algorithms that deal with this problem has been focused by many researchers, the main goal remains undefined: the understanding of generated clusters. As important as identifying clusters is to understand its meaning. A good cluster definition means a relevant understanding and can help the specialist to study or interpret data. Facing the problem of comprehend clusters - in other words, create labels - this paper presents a methodology to automatic labeling clusters based on techniques involving supervised and unsupervised learning plus a discretization model. Considering the problem from its inception, the problem of understanding clusters is dealt similar to a real problem, being initialized from clustering data. For this, an unsupervised learning technique is applied and then a supervised learning algorithm will detect which are the relevant attributes in order to define a specific cluster. Additionally, some strategies are used to create a methodology that presents a label (based on attributes and their values) for each cluster provided. Finally, this methodology is applied in four distinct databases presenting good results with an average above 88.79% of elements correctly labeled.