We consider mining unusual patterns from a set T of target texts. A typical method outputs unusual patterns if their observed frequencies are far from their expectation estimated under an assumed probabilistic model. However, it is difficult for the method to deal with the zero frequency and thus it suffers from data sparseness. We employ another set B of background texts to define a composition x y to be peculiar if both x and y are more frequent in B than in T and conversely xy is more frequent in T . xy is unusual because x and y are infrequent in T while xy is unexpectedly frequent compared to xy in B. To find frequent subpatterns and infrequent patterns simultaneously, we develop a fast algorithm using the suffix tree and show that it scales almost linearly under practical settings of parameters. Experiments using DNA sequences show that found peculiar compositions basically appear in rRNA while patterns found by an existing method seem not to relate to specific biological functions.We also showthat discovered patterns have similar lengths at which the distribution of frequencies of fixed length substrings begins to skew. This fact explains why our method can find long peculiar compositions.
All Science Journal Classification (ASJC) codes
- Information Systems
- Human-Computer Interaction
- Hardware and Architecture
- Artificial Intelligence