Efficient repeating pattern finding in music databases
Proceedings of the seventh international conference on Information and knowledge management
Efficient Feature Mining in Music Objects
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Theme and Non-Trivial Repeating Pattern Discovering in Music Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Incrementally Mining Recently Repeating Patterns over Data Streams
New Frontiers in Applied Data Mining
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In this paper, an efficient strategy for mining top-K non-trivial fault-tolerant repeating patterns (FT-RPs in short) with lengths no less than min_len from data sequences is provided. By extending the idea of appearing bit sequences, fault-tolerant appearing bit sequences are defined to represent the locations where candidate patterns appear in a data sequence with insertion/deletion errors being allowed. Two algorithms, named TFTRP-Mine(Top-K non-trivial FT-RPs Mining) and RE-TFTRP-Mine (REfinement of TFTRP-Mine), respectively, are proposed. Both of these two algorithms use the recursive formulas to obtain the fault-tolerant appearing bit sequence of a pattern systematically and then the fault-tolerant frequency of each candidate pattern could be counted quickly. Besides, RE-TFTRP-Mine adopts two additional strategies for pruning the searching space in order to improve the mining efficiency. The experimental results show that RE-TFTRP-Mine outperforms TFTRP-Mine algorithm when K and min_len are small. In addition, more important and implicit repeating patterns could be found from real music objects by adopting fault tolerant mining.