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MC-SC2 Publications about: clustering
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K. Jajuga,
A. Sokolowski,
and H.-H. Bock, editors.
Classification, Clustering and Data Analysis: Recent Advances and Applications,
Studies in Classification, Data Analysis & Knowledge Organization.
Springer-Verlag,
Berlin,
2002.
[bibtex-entry]
Articles in journal or book chapters
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K.-L. Chung and K.-S. Lin.
An efficient line symmetry-based k-means algorithm.
Pattern Recognition Letters,
27(7):765-772,
2006.
Note: Short Communication.
Keyword(s): clustering,
k-means algorithm,
point symmetry,
line symmetry.
[bibtex-entry]
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C. Eick,
A. Rouhana,
A. Bagherjeiran,
and R. Vilalta.
Using clustering to learn distance functions for supervised similarity assessment.
Engineering Applications of Artificial Intelligence,
19(4):395-401,
2006.
Keyword(s): distance function learning,
supervised clustering,
nearest neighbor.
[bibtex-entry]
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U. Gonzales-Barron and F. Butler.
A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis.
Journal of Food Engineering,
74(2):268-278,
2006.
Keyword(s): image analysis,
bread,
thresholding,
crumb features.
[bibtex-entry]
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Z. Knops,
J. Maintz,
M. Viergever,
and J. Pluim.
Normalized mutual information based registration using k-means clustering and shading correction.
Medical Image Analysis,
10(3):432-439,
2006.
Keyword(s): image registration,
normalized mutual information,
k-means clustering,
shading correction,
robustness.
[bibtex-entry]
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S. Mingoti and J. Lima.
Comparing SOM neural network with fuzzy c-means, k-means and traditional hierarchical clustering algorithms.
European Journal of Operational Research,
2006.
Note: In Press.
Keyword(s): multivariate statistics,
hierarchical clustering,
SOM neural network,
fuzzy c-means,
k- means.
[bibtex-entry]
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M. Otsubo,
K. Sato,
and A. Yamaji.
Computerized identification of stress tensors determined from heterogeneous fault-slip data by combining the multiple inverse method and k-means clustering.
Journal of Structural Geology,
28(6):991-997,
2006.
Keyword(s): stress tensor,
k-means,
multiple inverse method,
s-space,
stress difference,
meso-scale fault.
[bibtex-entry]
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G. Papamichail and D. Papamichail.
The k-means range algorithm for personalized data clustering in e-commerce.
European Journal of Operational Research,
2006.
Note: In Press.
Keyword(s): heuristics,
distributed consumer decision-making,
range search,
data clustering,
personalized systems.
[bibtex-entry]
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G. Peters.
Some refinements of rough k-means clustering.
Pattern Recognition,
39(8):1481-1491,
2006.
Keyword(s): cluster algorithms,
rough k-means,
soft computing,
data analysis,
forest data,
bioinformatics data.
[bibtex-entry]
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L. Rueda and Y. Zhang.
Geometric visualization of clusters obtained from fuzzy clustering algorithms.
Pattern Recognition,
39(8):1415-1429,
2006.
Keyword(s): fuzzy clustering,
fuzzy a-means,
cluster visualization,
expectation maximization.
[bibtex-entry]
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Y. Yang and M. Kamel.
An aggregated clustering approach using multi-ant colonies algorithms.
Pattern Recognition,
39(7):1278-1289,
2006.
Keyword(s): ant algorithm,
multi-ant colonies,
clustering,
aggregated clustering.
[bibtex-entry]
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A. Goktepe,
S. Altun,
and A. Sezer.
Soil clustering by fuzzy c-means algorithm.
Advances in Engineering Software,
36(10):691-698,
2005.
Keyword(s): fine grained soils,
fuzzy c-means,
hard k-means,
clustering.
[bibtex-entry]
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R.-J. Kuo,
H.-S. Wang,
T.-L. Hu,
and S.-H. Chou.
Application of ant k-means on clustering analysis.
Computers & Mathematics with Applications,
50(10-12):1709-1724,
2005.
Keyword(s): data mining,
clustering analysis,
ant colony optimization.
[bibtex-entry]
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Y. Marzouk and A. Ghoniem.
K-means clustering for optimal partitioning and dynamic load balancing of parallel hierarchical N-body simulations.
Journal of Computational Physics,
207(2):493-528,
2005.
Keyword(s): k-means clustering,
treecode,
n-body problems,
hierarchical methods,
parallel processing,
load balancing,
particle methods,
vortex methods,
three-dimensional flow,
transverse jet.
[bibtex-entry]
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J. Tian,
L. Zhu,
S. Zhang,
and L. Liu.
Improvement and parallelism of k-means clustering algorithm.
Tsinghua Science & Technology,
10(3):277-281,
2005.
Keyword(s): data mining,
cluster analysis,
k-means algorithm,
parallelism.
[bibtex-entry]
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C.-A. Tsai,
T.-C. Lee,
I.-C. Ho,
U.-C. Yang,
C.-H. Chen,
and J. Chen.
Multi-class clustering and prediction in the analysis of microarray data.
Mathematical Biosciences,
193(1):79-100,
2005.
Keyword(s): bagged clustering,
bagging fuzzy clustering,
gene selection,
k-nn classification,
rand statistic,
shaded similarity matrix plot.
[bibtex-entry]
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N. Belacel,
M. Cuperlovic-Culf,
M. Laflamme,
and R. Ouelette.
Fuzzy J-means and VNS methods for clustering genes from microarray data.
Bioinformatics Journal,
20:1690-1701,
2004.
[bibtex-entry]
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Y. De Smet and L. Montano Guzmán.
Towards multicriteria clustering: An extension of the k-means algorithm.
European Journal of Operational Research,
158(2):390-398,
2004.
Keyword(s): multiple criteria analysis,
clustering,
preference modelling,
k-means algorithm.
[bibtex-entry]
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T. Kanungo,
D. Mount,
N. Netanyahu,
C. Piatko,
R. Silverman,
and A. Wu.
A local search approximation algorithm for k-means clustering.
Computational Geometry,
28(2-3):89-112,
2004.
Keyword(s): clustering,
k-means,
approximation algorithms,
local search,
computational geometry.
[bibtex-entry]
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S. Khan and A. Ahmad.
Cluster center initialization algorithm for k-means clustering.
Pattern Recognition Letters,
25(11):1293-1302,
2004.
Note: Short Communication.
Keyword(s): k-means clustering,
initial cluster centers,
cost function,
density based multiscale data condensation.
[bibtex-entry]
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J. Sarkis and S. Talluri.
Performance based clustering for benchmarking of US airports.
Transportation Research Part A: Policy and Practice,
38(5):329-346,
2004.
Keyword(s): data envelopment analysis,
clustering,
benchmarking,
performance analysis.
[bibtex-entry]
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W.-J. Wang,
Y.-X. Tan,
J.-H. Jiang,
J.-Z. Lu,
G.-L. Shen,
and R.-Q. Yu.
Clustering based on kernel density estimation: nearest local maximum searching algorithm.
Chemometrics and Intelligent Laboratory Systems,
72(1):1-8,
2004.
Keyword(s): NLMSA,
pattern recognition,
cluster analysis,
kernel density estimation,
local optimization.
[bibtex-entry]
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Y.-M. Cheung.
K*-means: A new generalized k-means clustering algorithm.
Pattern Recognition Letters,
24(15):2883-2893,
2003.
Note: Short Communication.
Keyword(s): clustering analysis,
k-means algorithm,
cluster number,
rival penalization.
[bibtex-entry]
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A. Likas,
N. Vlassis,
and J. Verbeek.
The global k-means clustering algorithm.
Pattern Recognition,
36(2):451-461,
2003.
Keyword(s): clustering,
k-means algorithm,
global optimization,
k-d trees,
data mining.
[bibtex-entry]
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D. Modha and W. Spangler.
Feature weighting in k-means clustering.
Machine Learning,
52(3):217-237,
2003.
Keyword(s): clustering,
convexity,
convex k-means algorithm,
feature combination,
feature selection,
Fisher’s discriminant analysis,
text mining,
unsupervised learning.
[bibtex-entry]
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T. Tarpey and K. Kinateder.
Clustering functional data.
Journal of Classification,
20:93-114,
2003.
Keyword(s): Fourier basis,
Gaussian random functions,
k-means algorithm,
mean squared error,
principal components,
principal points.
[bibtex-entry]
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N. Belacel,
P. Hansen,
and N. Mladenovic.
Fuzzy J-means: A new heuristic for fuzzy clustering.
Pattern Recognition,
35(10):2193-2200,
2002.
Keyword(s): unsupervised classification,
fuzzy clustering,
local search,
fuzzy c-means,
variable neighbourhood search.
[bibtex-entry]
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R. Bisdorff.
ELECTRE-like clustering from a pairwise fuzzy proximity index.
European Journal of Operational Research,
138(2):320-331,
2002.
Keyword(s): multiple criteria analysis,
fuzzy clustering,
graph theory.
[bibtex-entry]
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A. Nola,
V. Loia,
and A. Staiano.
An evolutionary approach to spatial fuzzy c-means clustering.
Fuzzy Optimization and Decision Making,
1:195-219,
2002.
Keyword(s): clustering algorithm,
fuzzy c-means,
fuzzy sets,
genetic algorithm,
Java language.
[bibtex-entry]
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F. Questier,
I. Arnaut-Rollier,
B. Walczak,
and D. Massart.
Application of rough set theory to feature selection for unsupervised clustering.
Chemometrics and Intelligent Laboratory Systems,
63:155-167,
2002.
Keyword(s): rough sets,
unsupervised clustering,
feature selection,
Wallace measure.
[bibtex-entry]
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M. Vrahatis,
B. Boutsinas,
P. Alevizos,
and G. Pavlides.
The new k-windows algorithm for improving the k-means clustering algorithm.
Journal of Complexity,
18(1):375-391,
2002.
Keyword(s): k-means clustering algorithm,
unsupervised learning,
data mining,
range search.
[bibtex-entry]
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V. Ananthanarayana,
M. Murty,
and D. Subramanian.
Efficient clustering of large data sets.
Pattern Recognition,
34(12):2561-2563,
2001.
Note: Short Communication.
[bibtex-entry]
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V. Ananthanarayana,
M. Murty,
and D. Subramanian.
Multi-dimensional semantic clustering of large databases for association rule mining.
Pattern Recognition,
34(4):939-941,
2001.
Note: Short Communication.
[bibtex-entry]
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U. Maulik and S. Bandyopadhyay.
Genetic algorithm-based clustering technique.
Pattern Recognition,
33(9):1455-1465,
2000.
Keyword(s): genetic algorithm,
clustering metric,
k-means algorithm,
real encoding,
Euclidean distance.
[bibtex-entry]
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M. Ng.
A note on constrained k-means algorithms.
Pattern Recognition,
33(3):515-519,
2000.
Keyword(s): clustering,
constraints,
k-means algorithm,
PCB insertion.
[bibtex-entry]
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V. Chepoi and D. Dumitrescu.
Fuzzy clustering with structural constraints.
Fuzzy Sets and Systems,
105:91-97,
1999.
Keyword(s): cluster analysis,
fuzzy n-means algorithm with structural constraints,
multifacility location problem,
structure graph.
[bibtex-entry]
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Y. Shapira and I. Gath.
Feature selection for multiple binary classification problems.
Pattern Recognition Letters,
20:823-832,
1999.
Keyword(s): feature selection,
transpose projection,
alternative partitions,
clustering.
[bibtex-entry]
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P. Lagacherie,
D. Cazemier,
P. van Gaans,
and P. Burrough.
Fuzzy k-means clustering of fields in an elementary catchment and extrapolation to a larger area.
Geoderma,
77(2-4):197-216,
1997.
Keyword(s): hydrology,
fuzzy sets,
clustering.
[bibtex-entry]
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P. Lagacherie,
D. Cazemier,
P. van Gaans,
and P. Burrough.
Fuzzy k-means clustering of fields in an elementary catchment and extrapolation to a larger area.
Geoderma,
77(2-4):197-216,
1997.
Keyword(s): hydrology,
fuzzy sets,
France.
[bibtex-entry]
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Y. Won and S. Kim.
Multiple criteria clustering algorithm for solving the group technology problem with multiple process routings.
Computers & Industrial Engineering,
32(1):207-220,
1997.
[bibtex-entry]
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M. Al-Daoud and S. Roberts.
New methods for the initialisation of clusters.
Pattern Recognition Letters,
17(5):451-455,
1996.
Keyword(s): clustering,
cluster initialisation,
k-means algorithm.
[bibtex-entry]
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P. Rousseeuw,
L. Kaufman,
and E. Trauwaert.
Fuzzy clustering using scatter matrices.
Computational Statistics & Data Analysis,
23(1):135-151,
1996.
Keyword(s): ellipsoidal clusters,
fuzzy clustering,
industrial applications,
maximum likelihood,
SAND method.
[bibtex-entry]
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S. Ronen and O. Shenkar.
Clustering countries on attitudinal dimensions: A review and synthesis.
Academy of Management Review,
10(3):435-454,
1985.
Keyword(s): cluster analysis,
correlation (statistical),
econometrics,
random variables,
research,
statistics,
quantitative research.
[bibtex-entry]
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T. Lane.
A k-th nearest neighbour clustering procedure.
Journal of the Royal Statistical Society. Series B (Methodological),
45(3):362-368,
1983.
Keyword(s): clustering procedure,
high density clusters,
k-th nearest neighbour density estimation,
set-consistency.
[bibtex-entry]
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T. Lane.
A k-th nearest neighbour clustering procedure.
Journal of the Royal Statistical Society. Series B (Methodological),
45(3):362-368,
1983.
Keyword(s): clustering procedure,
high density clusters,
k-th nearest neighbour density estimation,
set-consistency.
[bibtex-entry]
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M. Wong.
A k-th nearest neighbour clustering procedure.
Journal of the Royal Statistical Society. Series B (Methodological),
45(3):362-368,
1983.
Keyword(s): clustering procedure,
high density clusters,
k-th nearest neighbour density estimation,
set-consistency.
[bibtex-entry]
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M. Wong.
A k-th nearest neighbour clustering procedure.
Journal of the Royal Statistical Society. Series B (Methodological),
45(3):362-368,
1983.
Keyword(s): clustering procedure,
high density clusters,
k-th nearest neighbour density estimation,
set-consistency.
[bibtex-entry]
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W. Rand.
Objective criteria for the evaluation of clustering methods.
Journal of the American Statistical Association,
66(336):846-850,
1971.
[bibtex-entry]
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W. Williams.
Principles of clustering.
Annual Review of Ecology and Systematics,
2:303-326,
1971.
[bibtex-entry]
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N. Belacel,
M. Cuperlovic-Culf,
and M. Boulassel.
The variable neighborhood search metaheuristic for fuzzy clustering CDNA microarray gene expression data.
In Proceedings of IASTED International Conference on Artificial Intelligence and Applications,
Innsbruck, Austria,
2004.
[bibtex-entry]
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Y. Guan,
A. Ghorbani,
and N. Belacel.
An unsupervised clustering algorithm for intrusion detection: Advances in artificial intelligence.
In 16th Conference of the Canadian Society for Computational Studies of Intelligence,
Ontario, Canada,
pages 616-117,
2003.
Halifax,
Springer-Verlag.
[bibtex-entry]
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Y. Guan,
A. Ghorbani,
and N. Belacel.
Y-means: A clustering method for intrusion detection.
In Canadian Conference on Electrical and Computer Engineering,
Montreal, Quebec, Canada,
2003.
[bibtex-entry]
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M. Kumar and N. Patel.
Clustering data with measurement errors.
Technical report RRR 12-2005,
RUTCOR, Rutgers Center for Operations Research,
New Jersey,
2005.
[bibtex-entry]
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J. Figueira,
Y. De Smet,
and J.-P. Brans.
MCDA methods for sorting and clustering problems: PROMETHEE TRI and PROMETHEE CLUSTER.
Technical report 12,
INESC Coimbra,
Coimbra,
2003.
Keyword(s): multiple criteria decision analysis,
sorting,
clustering,
PROMETHEE methodology.
[bibtex-entry]
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Last modified: Fri Dec 15 07:05:47 2006
Author: Juscelino Dias.
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