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

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

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.

Original language	English
Title of host publication	Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4-7
Number of pages	4
ISBN (Print)	0769522785, 9780769522784
DOIs	https://doi.org/10.1109/ICDM.2005.4
Publication status	Published - 1 Jan 2005
Externally published	Yes
Event	5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States Duration: 27 Nov 2005 → 30 Nov 2005

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Conference

Conference	5th IEEE International Conference on Data Mining, ICDM 2005
Country/Territory	United States
City	Houston, TX
Period	27/11/05 → 30/11/05

Access to Document

10.1109/ICDM.2005.4

Cite this

Halkidi, M., Gunopulos, D., Kumar, N., Vazirgiannis, M., & Domeniconi, C. (2005). A framework for semi-supervised learning based on subjective and objective clustering criteria. In Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005 (pp. 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",

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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. in 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., pp. 4-7, 5th IEEE International Conference on Data Mining, ICDM 2005, Houston, TX, United States, 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).

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

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AU - Kumar, N.

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

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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. In 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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