Branch-and-bound technique for solving optimal clustering

Fränti, Pasi; Virmajoki, Olli; Kaukoranta, Timo

doi:10.1109/icpr.2002.1048281

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…A cluster analysis based on Ward's Minimum Variance method and Euclidean distances was performed for each key function using the mean values of the first three principal components (Dumay et al, 2004). We used the merging cost to determine the best cut-off distance for each dendrogram (Fränti et al, 2002). To corroborate statistical differences among these groups, we used the first three PCA components and performed a multivariate analysis of variance (MANOVA) with Hotelling's pairwise comparisons, applying the Bonferroni correction.…”

Section: Methodsmentioning

confidence: 99%

Fish functional groups in a tropical wetland of the Yucatan Peninsula, Mexico

Córdova-Tapia

Zambrano

2016

Neotrop. ichthyol.

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The characterization of species' functional traits is a major step in the understanding and description of communities in natural habitats. The classification of species into functional groups is a useful tool to identify redundancy and uniqueness. We studied the fish community of a pristine freshwater wetland in the Sian Ka'an Biosphere Reserve by analysing two multidimensional functions: food acquisition and locomotion. We investigated changes in the functional group structure between habitats (permanent and temporary pools) and seasons (dry and wet). Six functional groups with different ecological characteristics were detected, two of which had high functional redundancy and three of them were represented by single species with unique ecological functions. In permanent pools during the dry season, functional group richness and diversity were lower, while evenness was higher. During the wet season, all functional groups were detected and similar functional group structure was found between habitats. These results suggest an effect of environmental filtering during the dry season and niche complementarity during the wet season.La caracterización de los rasgos funcionales de las especies es un paso importante en la comprensión y descripción de las comunidades en hábitats naturales. La clasificación de especies en grupos funcionales es una herramienta útil para identificar la redundancia y la especialización. Se estudió la comunidad de peces de un humedal prístino de agua dulce en la Reserva de la Biosfera de Sian Ka'an, analizando dos funciones multidimensionales: adquisición de alimento y la locomoción. Analizamos los cambios en la estructura de los grupos funcional entre hábitats (permanentes y temporales) y temporadas (lluvias y secas). Seis grupos funcionales con características ecológicas distintas fueron detectados, dos de los cuales presentaron una alta redundancia funcional y tres de ellos están representados únicamente por una especie con una función ecológica particular. En las pozas permanentes durante la temporada de secas, la riqueza y diversidad de grupos funcionales fueron menores; mientras que la equitatividad de grupos funcionales fue mayor. Durante la temporada de lluvias se detectaron todos los grupos funcionales y la estructura de los grupos fue similar entre los hábitat. Estos resultados sugieren que los filtros ambientales tienen un efecto durante la temporada de secas y la complementariedad de nicho durante la temporada de lluvias.

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Section: Methodsmentioning

confidence: 99%

Fish functional groups in a tropical wetland of the Yucatan Peninsula, Mexico

Córdova-Tapia

Zambrano

2016

Neotrop. ichthyol.

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“…In other words, there is no known polynomial time algorithm for finding the optimal solution. In principle, the optimal codebook can be solved by using branch-and-bound technique as proposed in [27]. The method, however, has exponential time complexity and is applicable for small clustering problems only.…”

Section: Vector Quantizationmentioning

confidence: 99%

Multilevel thresholding by fast PNN-based algorithm

Virmajoki

Fränti

2003

SPIE Proceedings

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We propose a fast PNN-based O(N log N) time algorithm for multilevel non-parametric thresholding, where N denotes the size of the image histogram. The proposed PNNbased multilevel thresholding algorithm is considerably faster than optimal thresholding. On a set of 8-16 bits per pixel real images, experimental results also reveal that the proposed method provides better quality than the Lloyd-Max quantizer alone. Since the time complexity of the proposed thresholding algorithm is loglinear, it is applicable in real-time image processing applications.

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“…In principle, the optimal codebook can be solved by using a branch-and-bound technique, as proposed in Ref. 27. The method, however, has exponential time complexity and is applicable for small clustering problems only.…”

Section: Vector Quantizationmentioning

confidence: 99%

Fast pairwise nearest neighbor based algorithm for multilevel thresholding

Virmajoki

Fränti

2003

J. Electron. Imaging

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We propose a fast pairwise nearest neighbor (PNN)based O(N log N) time algorithm for multilevel nonparametric thresholding, where N denotes the size of the image histogram. The proposed PNN-based multilevel thresholding algorithm is considerably faster than optimal thresholding. On a set of 8 to 16 bits-per-pixel real images, experimental results also reveal that the proposed method provides better quality than the Lloyd-Max quantizer alone. Since the time complexity of the proposed thresholding algorithm is log linear, it is applicable in real-time image processing applications.

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Branch-and-bound technique for solving optimal clustering

Cited by 9 publications

References 7 publications

Fish functional groups in a tropical wetland of the Yucatan Peninsula, Mexico

Fish functional groups in a tropical wetland of the Yucatan Peninsula, Mexico

Multilevel thresholding by fast PNN-based algorithm

Fast pairwise nearest neighbor based algorithm for multilevel thresholding

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