Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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With the rapid growth in application of series data mining, one important issue is discovering character patterns in larger data sets. Two limitations of previous works were the weak efficiency and rigid partition. In this paper, we introduce a novel pattern searching algorithm that using cloud models to implement concept hierarchies and data reduction. The reduction in this algorithm is based on symbolic mapping which uses cloud transformation method. Compared with other works, we make use of linguistic atoms to describe series character both specifically and holistically. Furthermore, being the fuzzy and probabilistic of cloud models, soft partition to continuous numeric attributes and the capability to data noise were realized. Normal segmentation method was done as comparison to show the performance of cloud models based algorithm. The efficiency is improved obviously. Moreover, noise-adding experiment was implemented to show that algorithm has robustness to the noise.