Causal rules detection in streams of unlabeled, mixed type values with finit domains

Abstract

Knowledge discovery from data streams in recent years become one of the most important research area in a domain of data science. This is mainly due to the rapid development of mobile devices, and Internet of things solutions which allow for obtaining petabytes of data within minutes. All of the modern approaches either use representation that is flat in time domain, or follow black-box model paradigm. This reduces the expressiveness of models and limits the intelligibility of the system. In this paper we present an algorithm for rule discovery that allows to capture temporal causalities between numeric and symbolic attributes.