TY - JOUR
T1 - The clustered causal state algorithm efficient pattern discovery for lossy data-compression applications
AU - Schmiedekamp, Mendel
AU - Subbu, Aparna
AU - Phoha, Shashi
N1 - Funding Information:
This material is based on work supported and administered by the US Office of Naval Research under the Semantics Source Coding project award no. N00014-03-1-0231. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the Office of Naval Research.
PY - 2006/9
Y1 - 2006/9
N2 - Clustered Causal State Algorithm (CCSA), a pattern discovery algorithm, is developed in lossy video compression to approximate E-machines, for use in real time and resource limited applications. CCSA performs unsupervised pattern discovery, producing pattern descriptions with computational efficiency for use in data compression in exchange for a small loss in description fidelity. It is based on the hierarchical agglomerative clustering method and attempts to describe patterns intrinsic to a process, which it achieves at a lower computational cost. The inputs to the CCSA program are the symbol stream and the algorithm executes in the following steps: initialization, clustering, finalization. CCSA has the distinct advantage of polynomial computational complexity, and using this algorithm image compression takes few seconds and it could reliably generate 10 to 20 fold compressions.
AB - Clustered Causal State Algorithm (CCSA), a pattern discovery algorithm, is developed in lossy video compression to approximate E-machines, for use in real time and resource limited applications. CCSA performs unsupervised pattern discovery, producing pattern descriptions with computational efficiency for use in data compression in exchange for a small loss in description fidelity. It is based on the hierarchical agglomerative clustering method and attempts to describe patterns intrinsic to a process, which it achieves at a lower computational cost. The inputs to the CCSA program are the symbol stream and the algorithm executes in the following steps: initialization, clustering, finalization. CCSA has the distinct advantage of polynomial computational complexity, and using this algorithm image compression takes few seconds and it could reliably generate 10 to 20 fold compressions.
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U2 - 10.1109/MCSE.2006.98
DO - 10.1109/MCSE.2006.98
M3 - Review article
AN - SCOPUS:33748302166
SN - 1521-9615
VL - 8
SP - 59
EP - 67
JO - Computing in Science and Engineering
JF - Computing in Science and Engineering
IS - 5
M1 - 1677484
ER -