Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
ISHPC'05/ALPS'06 Proceedings of the 6th international symposium on high-performance computing and 1st international conference on Advanced low power systems
Sequential pattern mining with time intervals
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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We propose a motif discovery system that uses a modified PrefixSpan method to extract frequent patterns from an annotated sequence database that has such attributes as a sequence identifier (sequence-id), a sequence, and a set of items. The annotations are represented as the set of items in the database. Frequent sequence patterns and frequent item patterns are extracted from the annotated sequence database. Frequent sequence patterns are located in both identical and non-identical positions among those sequences. In general, the existing PrefixSpan method can extract a large number of identical patterns from the sequence databases. However, the method does not include a function to extract frequent patterns together with gaps or wild character symbols. This new method allows the incorporation of gap characters. Moreover, the method allows effective handling of the annotated sequence database that consists of a set of tuples including a sequence together with a set of items. Furthermore, the prototype has been applied to the evaluation of three sets of sequences that include the Zinc Finger, Cytochrome C, and Kringle motifs.