Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Mining Sequential Patterns with Regular Expression Constraints
IEEE Transactions on Knowledge and Data Engineering
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth
ICDE '01 Proceedings of the 17th International Conference on Data Engineering
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
First-order temporal pattern mining with regular expression constraints
Data & Knowledge Engineering
Multi-Dimensional Relational Sequence Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
View learning for statistical relational learning: with an application to mammography
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Sequential pattern mining in multi-relational datasets
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
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In this work we present XMuSer, a multi-relational framework suitable to explore temporal patterns available in multirelational databases. XMuSer's main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequential miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems. Moreover, we map each one of three different types of sequential patterns: frequent sequences, closed sequences or maximal sequences.