A framework for semi-supervised learning based on subjective and objective clustering criteria

M. Halkidi; D. Gunopulos; N. Kumar; M. Vazirgiannis; C. Domeniconi

doi:10.1109/ICDM.2005.4

A framework for semi-supervised learning based on subjective and objective clustering criteria

M. Halkidi
, D. Gunopulos
, N. Kumar
, M. Vazirgiannis
, C. Domeniconi

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-constraints and the quality of intermediate clustering results in terms of its structural properties. It uses the clustering algorithm and the validity measure as parameters.

langue originale	Anglais
titre	Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	4-7
Nombre de pages	4
ISBN (imprimé)	0769522785, 9780769522784
Les DOIs	https://doi.org/10.1109/ICDM.2005.4
état	Publié - 1 janv. 2005
Modification externe	Oui
Evénement	5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, États-Unis Durée: 27 nov. 2005 → 30 nov. 2005

Série de publications

Nom	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (imprimé)	1550-4786

Une conférence

Une conférence	5th IEEE International Conference on Data Mining, ICDM 2005
Pays/Territoire	États-Unis
La ville	Houston, TX
période	27/11/05 → 30/11/05

Accès au document

10.1109/ICDM.2005.4

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Contient cette citation

Halkidi, M., Gunopulos, D., Kumar, N., Vazirgiannis, M., & Domeniconi, C. (2005). A framework for semi-supervised learning based on subjective and objective clustering criteria. Dans Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005 (p. 4-7). Article 1565745 (Proceedings - IEEE International Conference on Data Mining, ICDM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2005.4

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title = "A framework for semi-supervised learning based on subjective and objective clustering criteria",

abstract = "In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-constraints and the quality of intermediate clustering results in terms of its structural properties. It uses the clustering algorithm and the validity measure as parameters.",

author = "M. Halkidi and D. Gunopulos and N. Kumar and M. Vazirgiannis and C. Domeniconi",

year = "2005",

month = jan,

day = "1",

doi = "10.1109/ICDM.2005.4",

language = "English",

isbn = "0769522785",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005",

note = "5th IEEE International Conference on Data Mining, ICDM 2005 ; Conference date: 27-11-2005 Through 30-11-2005",

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Halkidi, M, Gunopulos, D, Kumar, N, Vazirgiannis, M & Domeniconi, C 2005, A framework for semi-supervised learning based on subjective and objective clustering criteria. Dans Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005., 1565745, Proceedings - IEEE International Conference on Data Mining, ICDM, Institute of Electrical and Electronics Engineers Inc., p. 4-7, 5th IEEE International Conference on Data Mining, ICDM 2005, Houston, TX, États-Unis, 27/11/05. https://doi.org/10.1109/ICDM.2005.4

A framework for semi-supervised learning based on subjective and objective clustering criteria. / Halkidi, M.; Gunopulos, D.; Kumar, N. et al.
Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005. Institute of Electrical and Electronics Engineers Inc., 2005. p. 4-7 1565745 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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T1 - A framework for semi-supervised learning based on subjective and objective clustering criteria

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AU - Vazirgiannis, M.

AU - Domeniconi, C.

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N2 - In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-constraints and the quality of intermediate clustering results in terms of its structural properties. It uses the clustering algorithm and the validity measure as parameters.

AB - In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-constraints and the quality of intermediate clustering results in terms of its structural properties. It uses the clustering algorithm and the validity measure as parameters.

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U2 - 10.1109/ICDM.2005.4

DO - 10.1109/ICDM.2005.4

M3 - Conference contribution

AN - SCOPUS:34548564268

SN - 0769522785

SN - 9780769522784

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

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Halkidi M, Gunopulos D, Kumar N, Vazirgiannis M, Domeniconi C. A framework for semi-supervised learning based on subjective and objective clustering criteria. Dans Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005. Institute of Electrical and Electronics Engineers Inc. 2005. p. 4-7. 1565745. (Proceedings - IEEE International Conference on Data Mining, ICDM). doi: 10.1109/ICDM.2005.4

A framework for semi-supervised learning based on subjective and objective clustering criteria

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Série de publications

Une conférence

Accès au document

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