A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Blind construction of optimal nonlinear recursive predictors for discrete sequences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Picking up the pieces: Causal states in noisy data, and how to recover them
Pattern Recognition Letters
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Pattern discovery is a potential boon for data compression. By identifying generic patterns without humansupervision, pattern discovery algorithms can extract the most relevant information for greatest fidelityin lossy compression. However, current approaches to pattern discovery are inefficient and producecumbersome descriptions of patterns. The Clustered Causal State Algorithm (CCSA) is a new pattern discovery algorithm incorporating recent clustering technology. This algorithmsacrifices accuracy for increased efficiency and smaller model sizes. This makes CCSA ideal for lossy datacompression and other real-time applications. This algorithm is compared to other pattern discoveryalgorithms and demonstrated in an image compression application.