The Clustered Causal State Algorithm: Efficient Pattern Discovery for Lossy Data-Compression Applications

摘要

Pattern discovery is a potential boon for data compression. By identifying generic patterns without human supervision, pattern discovery algorithms can extract the most relevant information for greatest fidelity in lossy compression. However, current approaches to pattern discovery are inefficient and produce cumbersome descriptions of patterns. The Clustered Causal State Algorithm (CCSA) is a new pattern discovery algorithm incorporating recent clustering technology. This algorithm sacrifices accuracy for increased efficiency and smaller model sizes. This makes CCSA ideal for lossy data compression and other real-time applications. This algorithm is compared to other pattern discovery algorithms and demonstrated in an image compression application.