### Abstract

This paper presents a new pattern discovery algorithm for constructing probabilistic finite state automata (PFSA) from symbolic sequences. The new algorithm, described as Compression via Recursive Identification of Self-Similar Semantics (CRISSiS), makes use of synchronizing strings for PFSA to localize particular states and then recursively identifies the rest of the states by computing the n-step derived frequencies. We compare our algorithm to other existing algorithms, such as D-Markov and Casual-State Splitting Reconstruction (CSSR) and show both theoretically and experimentally that our algorithm captures a larger class of models.

Original language | English (US) |
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Title of host publication | Proceedings of the 2011 American Control Conference, ACC 2011 |

Pages | 125-130 |

Number of pages | 6 |

State | Published - 2011 |

Event | 2011 American Control Conference, ACC 2011 - San Francisco, CA, United States Duration: Jun 29 2011 → Jul 1 2011 |

### Other

Other | 2011 American Control Conference, ACC 2011 |
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Country | United States |

City | San Francisco, CA |

Period | 6/29/11 → 7/1/11 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering

### Cite this

*Proceedings of the 2011 American Control Conference, ACC 2011*(pp. 125-130). [5991453]

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*Proceedings of the 2011 American Control Conference, ACC 2011.*, 5991453, pp. 125-130, 2011 American Control Conference, ACC 2011, San Francisco, CA, United States, 6/29/11.

**Unsupervised inductive learning in symbolic sequences via recursive identification of self-similar semantics.** / Chattopadhyay, Ishanu; Wen, Yicheng; Ray, Asok; Phoha, Shashi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Unsupervised inductive learning in symbolic sequences via recursive identification of self-similar semantics

AU - Chattopadhyay, Ishanu

AU - Wen, Yicheng

AU - Ray, Asok

AU - Phoha, Shashi

PY - 2011

Y1 - 2011

N2 - This paper presents a new pattern discovery algorithm for constructing probabilistic finite state automata (PFSA) from symbolic sequences. The new algorithm, described as Compression via Recursive Identification of Self-Similar Semantics (CRISSiS), makes use of synchronizing strings for PFSA to localize particular states and then recursively identifies the rest of the states by computing the n-step derived frequencies. We compare our algorithm to other existing algorithms, such as D-Markov and Casual-State Splitting Reconstruction (CSSR) and show both theoretically and experimentally that our algorithm captures a larger class of models.

AB - This paper presents a new pattern discovery algorithm for constructing probabilistic finite state automata (PFSA) from symbolic sequences. The new algorithm, described as Compression via Recursive Identification of Self-Similar Semantics (CRISSiS), makes use of synchronizing strings for PFSA to localize particular states and then recursively identifies the rest of the states by computing the n-step derived frequencies. We compare our algorithm to other existing algorithms, such as D-Markov and Casual-State Splitting Reconstruction (CSSR) and show both theoretically and experimentally that our algorithm captures a larger class of models.

UR - http://www.scopus.com/inward/record.url?scp=80053151144&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053151144&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781457700804

SP - 125

EP - 130

BT - Proceedings of the 2011 American Control Conference, ACC 2011

ER -