Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series
Sequence Learning - Paradigms, Algorithms, and Applications
Sequence Learning via Bayesian Clustering by Dynamics
Sequence Learning - Paradigms, Algorithms, and Applications
A compression-based algorithm for Chinese word segmentation
Computational Linguistics
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
Estimating Episodes of Care Using Linked Medical Claims Data
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Discovery of Core Episodes from Sequences
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Improving Sequence Recognition for Learning the Behavior of Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Contextual dependencies in unsupervised word segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Perception-based approach to time series data mining
Applied Soft Computing
Removing biases in unsupervised learning of sequential patterns
Intelligent Data Analysis
Voting experts: An unsupervised algorithm for segmenting sequences
Intelligent Data Analysis
Efficient algorithms for segmentation of item-set time series
Data Mining and Knowledge Discovery
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Rule generation for categorical time series with Markov assumptions
Statistics and Computing
Fully unsupervised word segmentation with BVE and MDL
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Word segmentation as general chunking
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Removing statistical biases in unsupervised sequence learning
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
| Hi-index | 0.00 |
This paper describes an unsupervised algorithm for segmenting categorical time series. The algorithm first collects statistics about the frequency and boundary entropy of ngrams, then passes a window over the series and has two "expert methods" decide where in the window boundaries should be drawn. The algorithm segments text into words successfully in three languages. We claim that the algorithm finds meaningful episodes in categorical time series, because it exploits two statistical characteristics of meaningful episodes.