Clustering Symbolic Time-Series using L-tuples
نویسندگان
چکیده
Among the many dimensionality reduction methods for timeseries data, Symbolic Aggregate approXimation (SAX) is perhaps the most popular due to its simplicity and uniqueness. With SAX, time-series data can be represented as string sequences which enables the utilization of methods found in text mining and bioinformatics to enhance data mining tasks. We propose an application of L-tuples to improve clustering SAX-represented time-series. Using the Ltuple frequency distributions of sequences, we compute dissimilarity based on maximum Kullback-Leibler divergence. We compare our new approach and dissimilarity measure with existing SAX measures and show that our dissimilarity measure with L-tuples is able to enhance the quality of clustering of time-series.
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