Generating implicit association rules from textual data

The need for sophisticated analysis of textual data is becoming very apparent. In the general context of knowledge discovery, text mining techniques aim to discover additional information from hidden patterns in unstructured large textual collections. Hence, we are interested especially in the extraction of the associations from unstructured databases. The objective is two fold. First, to propose a conceptual approach, based on the formal concept analysis (Ganter and Wille, 1999) and a semantic pruning, in order to discover implicit association rules, from large textual corpus. Second, to introduce an algorithm to derive additional and implicit association rules, using an associated taxonomy, from the already discovered association rules.