Generating Implicit Association Rules from Textual Data

Abstract: The need of sophisticated analysis of textual data is becoming very apparent. In the general context of knowledge discovery, Textmining techniques aim to discover additional information from hidden patterns in unstructured large textual collection. Hence, in this paper, we are interested especially in the extraction of the associations from unstructured database. The objective of this paper is twofold. First, to propose a conceptual approach, based on the formal concept analysis [11] and a semantic pruning, in order to discover explicit 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.