### Abstract

Semi-supervised clustering uses partially labeled data, as often occurs in practical clustering, to obtain a better clustering result. One approach uses hard constraints which specify data that must and cannot be within the same cluster. In this chapter, we propose another approach to semi-supervised clustering with soft pairwise constraints. The clustering method used is fuzzy c-means (FCM), a commonly used fuzzy clustering method. Two previously proposed variants, entropy- regularized relational/kernel fuzzy c-means clustering and indefinite kernel fuzzy c-means clustering algorithm are modified to use the soft constraints. In addition, a method is discussed that propagates pairwise constraints when the given constraints are not sufficient for obtaining the desired clustering result. Using some numerical examples, it is shown that the proposed algorithms obtain better clustering results.

Language | English |
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Title of host publication | Studies in Computational Intelligence |

Publisher | Springer Verlag |

Pages | 45-61 |

Number of pages | 17 |

Volume | 671 |

DOIs | |

State | Published - 2017 Jan 1 |

### Publication series

Name | Studies in Computational Intelligence |
---|---|

Volume | 671 |

ISSN (Print) | 1860949X |

### Fingerprint

### Keywords

- Fuzzy c-means
- Kernel
- Relational clustering
- Semi-supervised clustering

### ASJC Scopus subject areas

- Artificial Intelligence

### Cite this

*Studies in Computational Intelligence*(Vol. 671, pp. 45-61). (Studies in Computational Intelligence; Vol. 671). Springer Verlag. DOI: 10.1007/978-3-319-47557-8_4

**Semi-supervised fuzzy c-means algorithms by revising dissimilarity/kernel matrices.** / Kanzawa, Yuchi.

Research output: Research › Chapter

*Studies in Computational Intelligence.*vol. 671, Studies in Computational Intelligence, vol. 671, Springer Verlag, pp. 45-61. DOI: 10.1007/978-3-319-47557-8_4

}

TY - CHAP

T1 - Semi-supervised fuzzy c-means algorithms by revising dissimilarity/kernel matrices

AU - Kanzawa,Yuchi

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Semi-supervised clustering uses partially labeled data, as often occurs in practical clustering, to obtain a better clustering result. One approach uses hard constraints which specify data that must and cannot be within the same cluster. In this chapter, we propose another approach to semi-supervised clustering with soft pairwise constraints. The clustering method used is fuzzy c-means (FCM), a commonly used fuzzy clustering method. Two previously proposed variants, entropy- regularized relational/kernel fuzzy c-means clustering and indefinite kernel fuzzy c-means clustering algorithm are modified to use the soft constraints. In addition, a method is discussed that propagates pairwise constraints when the given constraints are not sufficient for obtaining the desired clustering result. Using some numerical examples, it is shown that the proposed algorithms obtain better clustering results.

AB - Semi-supervised clustering uses partially labeled data, as often occurs in practical clustering, to obtain a better clustering result. One approach uses hard constraints which specify data that must and cannot be within the same cluster. In this chapter, we propose another approach to semi-supervised clustering with soft pairwise constraints. The clustering method used is fuzzy c-means (FCM), a commonly used fuzzy clustering method. Two previously proposed variants, entropy- regularized relational/kernel fuzzy c-means clustering and indefinite kernel fuzzy c-means clustering algorithm are modified to use the soft constraints. In addition, a method is discussed that propagates pairwise constraints when the given constraints are not sufficient for obtaining the desired clustering result. Using some numerical examples, it is shown that the proposed algorithms obtain better clustering results.

KW - Fuzzy c-means

KW - Kernel

KW - Relational clustering

KW - Semi-supervised clustering

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U2 - 10.1007/978-3-319-47557-8_4

DO - 10.1007/978-3-319-47557-8_4

M3 - Chapter

VL - 671

T3 - Studies in Computational Intelligence

SP - 45

EP - 61

BT - Studies in Computational Intelligence

PB - Springer Verlag

ER -