Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The String-to-String Correction Problem
Journal of the ACM (JACM)
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
ACM Computing Surveys (CSUR)
ABC-SG: a new artificial bee colony algorithm-based distance of sequential data using sigma grams
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Similarity search of time series has attracted many researchers recently. In this scope, reducing the dimensionality of data is required to scale up the similarity search. Symbolic representation is a promising technique of dimensionality reduction, since it allows researchers to benefit from the richness of algorithms used for textual databases. To improve the effectiveness of similarity search we propose in this paper an extension to the edit distance that we call the extended edit distance. This new distance is applied to symbolic sequential data objects, and we test it on time series data bases in classification task experiments. We also prove that our distance is a metric.