### Abstract

In this paper, some semi-supervised clustering methods are proposed with two types of pair constraints: two data have to be together in the same cluster, and two data have to be in different clusters, which are classified into two types: one is based on the standard fuzzy c-means algorithm and the other is on the entropy regularized one. First, the standard fuzzy c-means and the entropy regularized one are introduced. Second, a pairwise constrained semi-supervised fuzzy c means are introduced, which is derived from pairwise constrained competitive agglomeration. Third, some new optimization problem are proposed, which are derived from adding new loss function of memberships to the original optimization problem, respectively. Last, an iterative algorithm is proposed by solving the optimization problem.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Pages | 268-281 |

Number of pages | 14 |

Volume | 5861 LNAI |

DOIs | |

Publication status | Published - 2009 |

Event | 6th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2009 - Awaji Island Duration: 2009 Nov 30 → 2009 Dec 2 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|

Volume | 5861 LNAI |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 6th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2009 |
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City | Awaji Island |

Period | 09/11/30 → 09/12/2 |

### Fingerprint

### Keywords

- Fuzzy c-means
- Pairwise constraints
- Semi-supervised clustering

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 5861 LNAI, pp. 268-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5861 LNAI). https://doi.org/10.1007/978-3-642-04820-3_25

**Some pairwise constrained semi-supervised fuzzy c-means clustering algorithms.** / Kanzawa, Yuchi; Endo, Yasunori; Miyamoto, Sadaaki.

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

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 5861 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5861 LNAI, pp. 268-281, 6th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2009, Awaji Island, 09/11/30. https://doi.org/10.1007/978-3-642-04820-3_25

}

TY - GEN

T1 - Some pairwise constrained semi-supervised fuzzy c-means clustering algorithms

AU - Kanzawa, Yuchi

AU - Endo, Yasunori

AU - Miyamoto, Sadaaki

PY - 2009

Y1 - 2009

N2 - In this paper, some semi-supervised clustering methods are proposed with two types of pair constraints: two data have to be together in the same cluster, and two data have to be in different clusters, which are classified into two types: one is based on the standard fuzzy c-means algorithm and the other is on the entropy regularized one. First, the standard fuzzy c-means and the entropy regularized one are introduced. Second, a pairwise constrained semi-supervised fuzzy c means are introduced, which is derived from pairwise constrained competitive agglomeration. Third, some new optimization problem are proposed, which are derived from adding new loss function of memberships to the original optimization problem, respectively. Last, an iterative algorithm is proposed by solving the optimization problem.

AB - In this paper, some semi-supervised clustering methods are proposed with two types of pair constraints: two data have to be together in the same cluster, and two data have to be in different clusters, which are classified into two types: one is based on the standard fuzzy c-means algorithm and the other is on the entropy regularized one. First, the standard fuzzy c-means and the entropy regularized one are introduced. Second, a pairwise constrained semi-supervised fuzzy c means are introduced, which is derived from pairwise constrained competitive agglomeration. Third, some new optimization problem are proposed, which are derived from adding new loss function of memberships to the original optimization problem, respectively. Last, an iterative algorithm is proposed by solving the optimization problem.

KW - Fuzzy c-means

KW - Pairwise constraints

KW - Semi-supervised clustering

UR - http://www.scopus.com/inward/record.url?scp=84886466638&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84886466638&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-04820-3_25

DO - 10.1007/978-3-642-04820-3_25

M3 - Conference contribution

AN - SCOPUS:84886466638

SN - 3642048196

SN - 9783642048197

VL - 5861 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 268

EP - 281

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -