An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
International Journal of Robotics Research
Incremental clustering of gesture patterns based on a self organizing incremental neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Machine learning approaches for time-series data based on self-organizing incremental neural network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Hi-index | 0.00 |
This paper proposes an on-line unsupervised segmentation algorithm of multidimensional time-series data. On-line unsupervised segmentation algorithm is important for discovery of frequent patterns from a large amount of multidimensional sensor data such as whole motion data and multi-modal data, because real-world problems are often dynamic and incremental:the input data may change over time and on-line segmentation methods are significantly faster than batch methods. We enhance the Sliding Window and Bottom-up (SWAB) algorithm toward the implementation of the segmentation algorithm of multidimensional data and propose MD-SWAB (Multi Dimensional SWAB). To evaluate the proposed algorithm, we use the continuous hand gesture data observed with a motion capture system. We have found that MD-SWAB outperforms a comparative algorithm in the segmentation performance and is significantly faster than the existing algorithm. In addition, we combine it with the on-line clustering method HB-SOINN to label the detected segments. The result of experiment shows that gesture patterns which are belong to same category are represented as similar label sequences.