The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Repeating pattern discovery and structure analysis from acoustic music data
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
ACM SIGMOD Record
Unsupervised pattern discovery for multimedia sequences
Unsupervised pattern discovery for multimedia sequences
Pattern Recognition Letters
Finding maximum-length repeating patterns in music databases
Multimedia Tools and Applications
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
IEEE Transactions on Audio, Speech, and Language Processing
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
Discovery of motifs to forecast outlier occurrence in time series
Pattern Recognition Letters
Hi-index | 0.10 |
This paper examines an unsupervised search method to discover motifs from multivariate time series data. Our method first scans the entire series to construct a list of candidate motifs in linear time, the list is then used to populate a sparse self-similarity matrix for further processing to generate the final selections. The proposed algorithm is efficient in both running time and memory storage. To demonstrate its effectiveness, we applied it to search for repeating segments in both music and sensory data sets. The experimental results showed that the proposed method can efficiently detect repeating segments as compared to well-known methods such as self-similarity matrix search and symbolic aggregation approximation approaches.