Gaussian hierarchical Bayesian clustering Algorithm

Rafael Eduardo Ruviaro Christ; Edwin Villanueva Talavera; Carlos Dias Maciel

doi:10.1109/ISDA.2007.4389598

Gaussian hierarchical Bayesian clustering Algorithm

Rafael Eduardo Ruviaro Christ, Edwin Villanueva Talavera, Carlos Dias Maciel

Universidade de São Paulo

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

Abstract

This paper presents the Gaussian Hierarchical Bayesian Clustering algorithm (GHBC). A new method for agglomerative hierarchical clustering derived from the HBC algorithm. GHBC has several advantages over traditional agglomerative algorithms. (1) It reduces the limitations due time and memory complexity. (2) It uses a bayesian posterior probability criterion to decide on merging clusters (modeling clusters as Gaussian distributions) rather than ad-hoc distance metrics. (3) It automatically finds the partition that most closely matches the data using Bayesian Information Criterion (BIC). Finally, experimental results on synthetic and real data show that GHBC can cluster data as the best classical agglomerative andpartitional algorithms.

Original language	English
Title of host publication	Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007
Pages	133-137
Number of pages	5
DOIs	https://doi.org/10.1109/ISDA.2007.4389598
State	Published - 2007
Externally published	Yes
Event	7th International Conference on Intelligent Systems Design and Applications, ISDA'07 - Rio de Janeiro, Brazil Duration: 22 Oct 2007 → 24 Oct 2007

Publication series

Name	Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007

Conference

Conference	7th International Conference on Intelligent Systems Design and Applications, ISDA'07
Country/Territory	Brazil
City	Rio de Janeiro
Period	22/10/07 → 24/10/07

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10.1109/ISDA.2007.4389598

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Christ, R. E. R., Talavera, E. V., & Maciel, C. D. (2007). Gaussian hierarchical Bayesian clustering Algorithm. In Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007 (pp. 133-137). Article 4389598 (Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007). https://doi.org/10.1109/ISDA.2007.4389598

Christ, Rafael Eduardo Ruviaro ; Talavera, Edwin Villanueva ; Maciel, Carlos Dias. / Gaussian hierarchical Bayesian clustering Algorithm. Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007. 2007. pp. 133-137 (Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007).

@inproceedings{df0cb378c1cf4f3f976cf61c6128052e,

title = "Gaussian hierarchical Bayesian clustering Algorithm",

abstract = "This paper presents the Gaussian Hierarchical Bayesian Clustering algorithm (GHBC). A new method for agglomerative hierarchical clustering derived from the HBC algorithm. GHBC has several advantages over traditional agglomerative algorithms. (1) It reduces the limitations due time and memory complexity. (2) It uses a bayesian posterior probability criterion to decide on merging clusters (modeling clusters as Gaussian distributions) rather than ad-hoc distance metrics. (3) It automatically finds the partition that most closely matches the data using Bayesian Information Criterion (BIC). Finally, experimental results on synthetic and real data show that GHBC can cluster data as the best classical agglomerative andpartitional algorithms.",

author = "Christ, {Rafael Eduardo Ruviaro} and Talavera, {Edwin Villanueva} and Maciel, {Carlos Dias}",

year = "2007",

doi = "10.1109/ISDA.2007.4389598",

language = "English",

isbn = "0769529763",

series = "Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007",

pages = "133--137",

booktitle = "Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007",

note = "7th International Conference on Intelligent Systems Design and Applications, ISDA'07 ; Conference date: 22-10-2007 Through 24-10-2007",

}

Christ, RER, Talavera, EV & Maciel, CD 2007, Gaussian hierarchical Bayesian clustering Algorithm. in Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007., 4389598, Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007, pp. 133-137, 7th International Conference on Intelligent Systems Design and Applications, ISDA'07, Rio de Janeiro, Brazil, 22/10/07. https://doi.org/10.1109/ISDA.2007.4389598

Gaussian hierarchical Bayesian clustering Algorithm. / Christ, Rafael Eduardo Ruviaro; Talavera, Edwin Villanueva; Maciel, Carlos Dias.
Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007. 2007. p. 133-137 4389598 (Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007).

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

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N2 - This paper presents the Gaussian Hierarchical Bayesian Clustering algorithm (GHBC). A new method for agglomerative hierarchical clustering derived from the HBC algorithm. GHBC has several advantages over traditional agglomerative algorithms. (1) It reduces the limitations due time and memory complexity. (2) It uses a bayesian posterior probability criterion to decide on merging clusters (modeling clusters as Gaussian distributions) rather than ad-hoc distance metrics. (3) It automatically finds the partition that most closely matches the data using Bayesian Information Criterion (BIC). Finally, experimental results on synthetic and real data show that GHBC can cluster data as the best classical agglomerative andpartitional algorithms.

AB - This paper presents the Gaussian Hierarchical Bayesian Clustering algorithm (GHBC). A new method for agglomerative hierarchical clustering derived from the HBC algorithm. GHBC has several advantages over traditional agglomerative algorithms. (1) It reduces the limitations due time and memory complexity. (2) It uses a bayesian posterior probability criterion to decide on merging clusters (modeling clusters as Gaussian distributions) rather than ad-hoc distance metrics. (3) It automatically finds the partition that most closely matches the data using Bayesian Information Criterion (BIC). Finally, experimental results on synthetic and real data show that GHBC can cluster data as the best classical agglomerative andpartitional algorithms.

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DO - 10.1109/ISDA.2007.4389598

M3 - Conference contribution

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T3 - Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007

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BT - Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007

T2 - 7th International Conference on Intelligent Systems Design and Applications, ISDA'07

Y2 - 22 October 2007 through 24 October 2007

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Gaussian hierarchical Bayesian clustering Algorithm

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