Visual text mining using association rules

In many situations, individuals or groups of individuals are faced with the need to examine sets of documents to achieve understanding of their structure and to locate relevant information. In that context, this paper presents a framework for visual text mining to support exploration of both general structure and relevant topics within a textual document collection. Our approach starts by building a visualization from the text data set. On top of that, a novel technique is presented that generates and filters association rules to detect and display topics from a group of documents. Results have shown a very consistent match between topics extracted using this approach to those actually present in the data set.