Data mining: concepts and techniques
Data mining: concepts and techniques
Locally adaptive dimensionality reduction for indexing large time series databases
ACM Transactions on Database Systems (TODS)
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
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
IEEE Transactions on Knowledge and Data Engineering
Data Mining in Time Series Database
Data Mining in Time Series Database
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing time series discretization for knowledge discovery
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Discretization of time series dataset using relative frequency and K-nearest neighbor approach
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Times series discretization using evolutionary programming
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
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In this work we propose a new approach to the discretization of time series using an approach that applies genetic algorithm operations called GENEBLA. The basic idea is to minimize the entropy of the temporal patterns over their class labels, follow a genetic search approach that allows to find good solutions more quickly to explore a wide variety of possible ways to solve the problem at the same time. The performance of GENEBLA was evaluated using twenty temporal datasets and compared to an efficient time series discretization algorithm called SAX and EBLA3 algorithm that shows similar representation.