### Abstract

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.

Original language | English (US) |
---|---|

Article number | 1677484 |

Pages (from-to) | 59-67 |

Number of pages | 9 |

Journal | Computing in Science and Engineering |

Volume | 8 |

Issue number | 5 |

DOIs | |

State | Published - Sep 1 2006 |

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### All Science Journal Classification (ASJC) codes

- Computer Science Applications
- Computational Theory and Mathematics

### Cite this

*Computing in Science and Engineering*,

*8*(5), 59-67. [1677484]. https://doi.org/10.1109/MCSE.2006.98

}

*Computing in Science and Engineering*, vol. 8, no. 5, 1677484, pp. 59-67. https://doi.org/10.1109/MCSE.2006.98

**The clustered causal state algorithm efficient pattern discovery for lossy data-compression applications.** / Schmiedekamp, Mendel; Subbu, Aparna; Phoha, Shashi.

Research output: Contribution to journal › Review article

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

PY - 2006/9/1

Y1 - 2006/9/1

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.

UR - http://www.scopus.com/inward/record.url?scp=33748302166&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33748302166&partnerID=8YFLogxK

U2 - 10.1109/MCSE.2006.98

DO - 10.1109/MCSE.2006.98

M3 - Review article

VL - 8

SP - 59

EP - 67

JO - Computing in Science and Engineering

JF - Computing in Science and Engineering

SN - 1521-9615

IS - 5

M1 - 1677484

ER -