We show how frequently occurring sequential patterns may be found from large datasets by first inducing a finite state automaton model describing the data, and then querying the m...
In this work we focus on the problem of frequent itemset mining on large, out-of-core data sets. After presenting a characterization of existing out-of-core frequent itemset minin...
The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from relational sequences. The great variety of applications of sequential pattern m...
Floriana Esposito, Nicola Di Mauro, Teresa Maria A...
Abstract. In this paper, an efficient strategy for mining top-K non-trivial faulttolerant repeating patterns (FT-RPs in short) with lengths no less than min_len from data sequences...
In this paper we investigate the general problem of discovering recurrent patterns that are embedded in categorical sequences. An important real-world problem of this nature is mo...