The Jaccard Similarity Mean

The arithmetic mean plays a central role in science and technology, being directly related to the concepts of statistical expectance and centrality. Yet, it is highly susceptible to the presence of outliers or biased interference in the original dataset to which it is applied. Described recently, the concept of similarity means has been preliminary found to have marked robustness to those same effects, especially when adopting the Jaccard similarity index. The present work is aimed at investigating further the properties of similarity means, especially regarding their range, translating and scaling properties, sensitivity and robustness to outliers. Several interesting contributions are reported, including an effective algorithm for obtaining the similarity mean, the analytic and experimental identification of a number of properties, as well as the confirmation of the potential stability of the similarity mean to the presence of outliers. The present work also describes an application case-example in which the Jaccard similarity is successfully employed to study cycles of sunspots, with interesting results.