Fast discovery of association rules
Advances in knowledge discovery and data mining
Finding similar regions in many strings
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Finding motifs using random projections
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
On the closest string and substring problems
Journal of the ACM (JACM)
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Spelling Approximate Repeated or Common Motifs Using a Suffix Tree
LATIN '98 Proceedings of the Third Latin American Symposium on Theoretical Informatics
Distinguishing string selection problems
Information and Computation
On the complexity of finding common approximate substrings
Theoretical Computer Science
Discovering motifs in DNA sequences
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Optimal Solutions for the Closest-String Problem via Integer Programming
INFORMS Journal on Computing
A frequent pattern mining method for finding planted (l, d)-motifs of unknown length
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
| Hi-index | 0.00 |
Common substring problems allowing errors are known to be NP-hard. The main challenge of the problems lies in the combinatorial explosion of potential candidates. In this paper, we propose and study a Generalized Center String (GCS) problem, where not only all models (center strings) of any length, but also the positions of all their (degenerative) instances in input sequences are searched for. Inspired by frequent pattern mining techniques in data mining field, we present an exact and efficient method to solve GCS. First, a highly parallelized TRIE-like structure, consensus tree, is proposed. Based on this structure, we present three Bpriori algorithms step by step. Bpriori algorithms can solve GCS with reasonable time and/or space complexities. We have proved that GCS is fixed parameter tractable with respect to fixed symbol set size and fixed length of input sequences. Experiment results on both artificial and real data have shown the correctness of the algorithms and the validity of our complexity analysis. A comparison with some current algorithms for solving Common Approximate Substring problems is also given.