On the discovery of semantically enhanced sequential patterns

Whereas the early frequent pattern mining methods admitted only relatively simple data and pattern formats (e.g., sets, sequences, etc.), there is nowadays a clear push towards the integration of ever larger portions of domain knowledge in the mining process in order to increase the precision and the abstraction level of the retrieved patterns and hence ease their interpretation. We present here a practically motivated study of a frequent pattern extraction from sequences of data objects that are described within a domain ontology. As the complexity of the descriptive structures is high, an entire framework for the pattern extraction process had to be defined. The key elements thereof are a pair of descriptive languages, one for individual data and another one for generic patterns, a generality relation between patterns, and an Apriori-like method for pattern mining.