Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive query processing for time-series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic models for nonstationary signal segmentation
Computers and Biomedical Research
Discovering similar patterns in time series
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
On the approximation of curves by line segments using dynamic programming
Communications of the ACM
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Minimum Message Length Segmentation
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Unsupervised Segmentation of Categorical Time Series into Episodes
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Evolutionary Time Series Segmentation for Stock Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A Time Series Analysis of Microarray Data
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Making Subsequence Time Series Clustering Meaningful
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Similarity Search over Future Stream Time Series
IEEE Transactions on Knowledge and Data Engineering
Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters
IEEE Transactions on Knowledge and Data Engineering
Novel Online Methods for Time Series Segmentation
IEEE Transactions on Knowledge and Data Engineering
Detection of Shape Anomalies: A Probabilistic Approach Using Hidden Markov Models
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An evolutionary approach to pattern-based time series segmentation
IEEE Transactions on Evolutionary Computation
A method for segmentation of switching dynamic modes in time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Spatio-temporal reasoning for the classification of satellite image time series
Pattern Recognition Letters
Modelling and recognition of signed expressions using subunits obtained by data---driven approach
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
A time-dependent enhanced support vector machine for time series regression
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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A time series is composed of lots of data points, each of which represents a value at a certain time. Many phenomena can be represented by time series, such as electrocardiograms in medical science, gene expressions in biology and video data in multimedia. Time series have thus been an important and interesting research field due to their frequent appearance in different applications. This paper proposes a time series segmentation approach by combining the clustering technique, the discrete wavelet transformation and the genetic algorithm to automatically find segments and patterns from a time series. The genetic algorithm is used to find the segmentation points for deriving appropriate patterns. In fitness evaluation, the proposed approach first divides the segments in a chromosome into k groups according to their slopes by using clustering techniques. The Euclidean distance is then used to calculate the distance of each subsequence and evaluate a chromosome. The discrete wavelet transformation is also used to adjust the length of the subsequences for calculating the similarity since their length may be different. The evaluation results are utilized to choose appropriate chromosomes for mating. The offspring then undergo recursive evolution until a good result has been obtained. Experimental results show that the proposed approach can get good results in finding appropriate segmentation patterns in time series.