Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A binary decision diagram based approach for mining frequent subsequences
Knowledge and Information Systems
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In this paper, we present an implementation of Erlang of an efficient index structure, called Sequence Binary Decision Diagrams (SeqBDDs), for knowledge discovery in large sequence data. Recently, Loekito, Bailey, and Pei (KAIS, 2009) proposed SeqBDD. SeqBDDs are a compact indices for efficiently representing the set of sequences. Furthermore, SeqBDDs provide a rich collection of operations for sets of sequences, which are useful for implementing sequence mining algorithms. We propose SeqBDDs as powerful framework for string processing and Erlang is appropriate language for SeqBDD. SeqBDD system heavily uses hash tables to avoid redundant memory and computation. We implemented SeqBDD package with ETS for hash tables.