Segment-based approach for subsequence searches in sequence databases
Proceedings of the 2001 ACM symposium on Applied computing
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
A Hidden Markov Model-Based Approach to Sequential Data Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Supporting Content-Based Searches on Time Series via Approximation
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
A Piecewise Linear Representation Method of Time Series Based on Feature Points
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Analysis and comparative evaluation of discrete tangent estimators
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
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In this paper, a novelty methodology for the representation and similarity measurement of sequential data is presented. First, a linear segmentation algorithm based on feature points is proposed. Then, two similarity measures are defined from the differences between the behavior and the mean level of the sequential data. These similarities are calculated for clustering and outlier detection of subjective sequential data generated through the evaluation of the driving risk obtained from a group of traffic safety experts. Finally, a novel dissimilarity measure for outlier detection of paired sequential data is proposed. The results of the experiments show that both similarities contain complementary and relevant information about the dataset. The methodology results useful to find patterns on subjective data related with the behavior and the level of the data.