The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo consideramos o k-problema de clustering para um conjunto S of n pontos pi=(xi) Na despaço tridimensional com erros baseados em variância como critérios de agrupamento, motivado a partir do problema de quantização de cores de calcular uma tabela de consulta de cores para exibição de buffer de quadros. Como critério intercluster a ser minimizado, é usada a soma dos erros intra-cluster em cada cluster, e como critério intra-cluster de um cluster Sj|Sj|α-1 Σpi
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Mary INABA, Naoki KATOH, Hiroshi IMAI, "Variance-Based k-Clustering Algorithms by Voronoi Diagrams and Randomization" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 6, pp. 1199-1206, June 2000, doi: .
Abstract: In this paper we consider the k-clustering problem for a set S of n points pi=(xi) in the d-dimensional space with variance-based errors as clustering criteria, motivated from the color quantization problem of computing a color lookup table for frame buffer display. As the inter-cluster criterion to minimize, the sum of intra-cluster errors over every cluster is used, and as the intra-cluster criterion of a cluster Sj, |Sj|α-1 Σpi
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_6_1199/_p
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@ARTICLE{e83-d_6_1199,
author={Mary INABA, Naoki KATOH, Hiroshi IMAI, },
journal={IEICE TRANSACTIONS on Information},
title={Variance-Based k-Clustering Algorithms by Voronoi Diagrams and Randomization},
year={2000},
volume={E83-D},
number={6},
pages={1199-1206},
abstract={In this paper we consider the k-clustering problem for a set S of n points pi=(xi) in the d-dimensional space with variance-based errors as clustering criteria, motivated from the color quantization problem of computing a color lookup table for frame buffer display. As the inter-cluster criterion to minimize, the sum of intra-cluster errors over every cluster is used, and as the intra-cluster criterion of a cluster Sj, |Sj|α-1 Σpi
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - Variance-Based k-Clustering Algorithms by Voronoi Diagrams and Randomization
T2 - IEICE TRANSACTIONS on Information
SP - 1199
EP - 1206
AU - Mary INABA
AU - Naoki KATOH
AU - Hiroshi IMAI
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2000
AB - In this paper we consider the k-clustering problem for a set S of n points pi=(xi) in the d-dimensional space with variance-based errors as clustering criteria, motivated from the color quantization problem of computing a color lookup table for frame buffer display. As the inter-cluster criterion to minimize, the sum of intra-cluster errors over every cluster is used, and as the intra-cluster criterion of a cluster Sj, |Sj|α-1 Σpi
ER -