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
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
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Genetic algorithms-based symbolic aggregate approximation
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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Similarity search in time series data mining is a problem that has attracted increasing attention recently. The high dimensionality and large volume of time series databases make sequential scanning inefficient to tackle this problem. There are many representation techniques that aim at reducing the dimensionality of time series so that the search can be handled faster at a lower dimensional space level. Symbolic representation is one of the promising techniques, since symbolic representation methods try to benefit from the wealth of search algorithms used in bioinformatics and text mining communities. The symbolic aggregate approximation (SAX) is one of the most competitive methods in the literature. SAX utilizes a similarity measure that is easy to compute because it is based on pre-computed distances obtained from lookup tables. In this paper we present a new similarity measure that is almost as easy to compute as the original similarity measure, but it is tighter because it uses updated lookup tables. In addition, the new similarity measure is more intuitive than the original one. We conduct several experiments which show that the new similarity measure gives better results than the original one.