Schizophrenia is a chronic neurobiological disorder whose early detection has attracted significant attention from the clinical, psychiatric and also artificial intelligence communities. This latter approach has been mainly focused on the analysis of neuroimaging and genetic data. A less explored strategy consists in exploiting the power of Natural Language Processing (NLP) algorithms applied over narrative texts produced by schizophrenic subjects. In this article, a novel dataset collected from a proper field study is presented. Also, gramatical traits discovered in narrative documents are used to build computational representations of texts, allowing an automatic classification of discourses generated by Schizophrenic and non-Schizophrenic subjects. The attained results showed that the use of the proposed computational representations along with Machine Learning techniques enables a novel and precise strategy to automatically detect texts produced by schizophrenic subjects.

The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the booming number of multi-view data, but also of clustering algorithms capable of producing a wide variety of representations for the same objects, merging clustering partitions to achieve a single clustering result has become a complex problem with numerous applications. To tackle this problem, we propose a clustering fusion algorithm that takes existing clustering partitions acquired from multiple vector space models, sources, or views, and merges them into a single partition. Our merging method relies on an information theory model based on Kolmogorov complexity that was originally proposed for unsupervised multi-view learning. Our proposed algorithm features a stable merging process and shows competitive results over several real and artificial datasets in comparison with other state-of-the-art methods that have similar goals.