Initial commit V1

2021-11-24 14:16:14 +01:00 · 2021-11-24 14:16:14 +01:00 · 5122467dfe
commit 5122467dfe
137 changed files with 563808 additions and 0 deletions
--- a/.gitignore
+++ b/.gitignore
--- a/README.md
+++ b/README.md
--- a/artificial/2d-10c.arff
+++ b/artificial/2d-10c.arff
--- a/artificial/2d-20c-no0.arff
+++ b/artificial/2d-20c-no0.arff
--- a/artificial/2d-3c-no123.arff
+++ b/artificial/2d-3c-no123.arff
@ -0,0 +1,727 @@
+% Data from J. Handl
+% http://personalpages.manchester.ac.uk/mbs/Julia.Handl/generators.html
+%
+@RELATION 2d-3c-no123
+
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--- a/artificial/2d-4c-no4.arff
+++ b/artificial/2d-4c-no4.arff
@ -0,0 +1,873 @@
+% Data from J. Handl
+% http://personalpages.manchester.ac.uk/mbs/Julia.Handl/generators.html
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--- a/artificial/2d-4c-no9.arff
+++ b/artificial/2d-4c-no9.arff
@ -0,0 +1,886 @@
+% Data from J. Handl
+% http://personalpages.manchester.ac.uk/mbs/Julia.Handl/generators.html
+%
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--- a/artificial/2d-4c.arff
+++ b/artificial/2d-4c.arff
--- a/artificial/2dnormals.arff
+++ b/artificial/2dnormals.arff
--- a/artificial/2sp2glob.arff
+++ b/artificial/2sp2glob.arff
--- a/artificial/3-spiral.arff
+++ b/artificial/3-spiral.arff
@ -0,0 +1,324 @@
+% Spiral, N=312, M=3, D=2
+% Chang, H. and D.Y. Yeung, Robust path-based spectral clustering. Pattern Recognition, 2008. 41(1): p. 191-203.
+%
+% source: http://cs.joensuu.fi/sipu/datasets/
+%
+@relation 3-spiral
+
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+@ATTRIBUTE y REAL
+@ATTRIBUTE class {1,2,3}
+
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--- a/artificial/3MC.arff
+++ b/artificial/3MC.arff
@ -0,0 +1,412 @@
+% Dataset from: http://mail.tku.edu.tw/chchou/others/others.htm
+%
+% M. C. Su, C. H. Chou, and C. C. Hsieh, “Fuzzy C-Means Algorithm with a Point Symmetry Distance,” 
+% International Journal of Fuzzy Systems, vol. 7, no. 4, pp. 175-181, 2005.
+%
+@RELATION 3MC
+
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE class {1,2,3}
+
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--- a/artificial/D31.arff
+++ b/artificial/D31.arff
--- a/artificial/DS-577.arff
+++ b/artificial/DS-577.arff
@ -0,0 +1,589 @@
+% Dataset from: http://mail.tku.edu.tw/chchou/others/others.htm
+%
+% M. C. Su, C. H. Chou, and C. C. Hsieh, “Fuzzy C-Means Algorithm with a Point Symmetry Distance,”
+% International Journal of Fuzzy Systems, vol. 7, no. 4, pp. 175-181, 2005.
+%
+@RELATION DS-577
+
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE class {1,2,3}
+
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--- a/artificial/DS-850.arff
+++ b/artificial/DS-850.arff
@ -0,0 +1,862 @@
+% Dataset from: http://mail.tku.edu.tw/chchou/others/others.htm
+%
+% M. C. Su, C. H. Chou, and C. C. Hsieh, “Fuzzy C-Means Algorithm with a Point Symmetry Distance,”
+% International Journal of Fuzzy Systems, vol. 7, no. 4, pp. 175-181, 2005.
+%
+@RELATION DS-850
+%
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE CLASS {0,1,2,3,4}
+%
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--- a/artificial/R15.arff
+++ b/artificial/R15.arff
@ -0,0 +1,610 @@
+% R15, N=600, M=15, D=2
+% Veenman, C.J., M.J.T. Reinders, and E. Backer, A maximum variance cluster algorithm. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002. 24(9): p. 1273-1280.
+%
+@RELATION R15
+
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+
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--- a/artificial/aggregation.arff
+++ b/artificial/aggregation.arff
@ -0,0 +1,800 @@
+% Aggregation
+% N=788, M=7, D=2
+% Gionis, A., H. Mannila, and P. Tsaparas, Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 2007. 1(1): p. 1-30.
+% source: http://cs.joensuu.fi/sipu/datasets/
+%
+@relation aggregation
+
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE class {1,2,3,4,5,6,7}
+
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--- a/artificial/aml28.arff
+++ b/artificial/aml28.arff
@ -0,0 +1,819 @@
+% The variant allele frequencies of somatic single nucleotide variants
+% from whole genome sequencing of a AML patient at two time points
+%
+% Miller, C. A., White, B. S., Dees, N. D., Griffith, M., Welch, J. S., Griffith, O. L.,
+% Vij, R., Tomasson, M. H., Graubert, T. A., Walter, M. J., et al. (2014).
+% SciClone: Inferring clonal architecture and tracking the spatial and temporal patterns
+% of tumor evolution. PLoS Comput. Biol., 10(8).
+% 
+@relation aml28
+
+@ATTRIBUTE tumor REAL
+@ATTRIBUTE relapse REAL
+@ATTRIBUTE class {1,2,3,4,5}
+
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--- a/artificial/atom.arff
+++ b/artificial/atom.arff
@ -0,0 +1,820 @@
+% This Fundamental Clustering Problems Suite (FCPS) may serve as a minimal test for new invented cluster algorithms.
+%
+% Every new invented algorithm should at least cluster the FCPS correctly.
+%
+% The data sets may be used as long as the following citation is referenced in any publication
+% concering any of this data:
+%
+% Alfred Ultsch, Clustering with SOM: U*C, in Proc. Workshop on Self Organizing Feature Maps ,pp 31-37 Paris 2005.
+%
+% main problem: different variances and linear not separable
+%
+@relation atom
+
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+@ATTRIBUTE class {1,2}
+
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+
--- a/artificial/banana.arff
+++ b/artificial/banana.arff
--- a/artificial/birch-rg1.arff
+++ b/artificial/birch-rg1.arff
--- a/artificial/birch-rg2.arff
+++ b/artificial/birch-rg2.arff
--- a/artificial/birch-rg3.arff
+++ b/artificial/birch-rg3.arff
--- a/artificial/blobs.arff
+++ b/artificial/blobs.arff
@ -0,0 +1,311 @@
+% Data generated using Python package 'sklearn' (sklearn.datasets.samples_generator)
+%
+% make_blobs(n_samples=300, centers=centers, cluster_std=0.5, random_state=0)
+%
+@relation blobs
+
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE class {0,1,2}
+
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--- a/artificial/bruit.arff
+++ b/artificial/bruit.arff
--- a/artificial/bruite_convexe.arff
+++ b/artificial/bruite_convexe.arff
@ -0,0 +1,314 @@
+% Toy dataset from http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html
+%
+% Zelnik-Manor, Lihi, and Pietro Perona. "Self-tuning spectral clustering." Advances in neural information processing systems. 2004.
+%
+@RELATION zelnik2
+%
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+@ATTRIBUTE y REAL
+@ATTRIBUTE CLASS {0,1,2}
+%
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--- a/artificial/carre.arff
+++ b/artificial/carre.arff
--- a/artificial/cassini.arff
+++ b/artificial/cassini.arff
--- a/artificial/chainlink.arff
+++ b/artificial/chainlink.arff
--- a/artificial/circle.arff
+++ b/artificial/circle.arff
--- a/artificial/cluto-t4-8k.arff
+++ b/artificial/cluto-t4-8k.arff
--- a/artificial/cluto-t5-8k.arff
+++ b/artificial/cluto-t5-8k.arff
--- a/artificial/cluto-t7-10k.arff
+++ b/artificial/cluto-t7-10k.arff
--- a/artificial/cluto-t8-8k.arff
+++ b/artificial/cluto-t8-8k.arff
--- a/artificial/complex8.arff
+++ b/artificial/complex8.arff
--- a/artificial/complex9.arff
+++ b/artificial/complex9.arff
--- a/artificial/compound.arff
+++ b/artificial/compound.arff
@ -0,0 +1,410 @@
+% Compound
+% N=399, M=6, D=2
+% Zahn, C.T., Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers, 1971. 100(1): p. 68-86.
+% source: http://cs.joensuu.fi/sipu/datasets/
+@relation compound
+
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+@ATTRIBUTE y REAL
+@ATTRIBUTE class {1,2,3,4,5,6}
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--- a/artificial/convexe_bien_separe.arff
+++ b/artificial/convexe_bien_separe.arff
--- a/artificial/convexe_identifie.arff
+++ b/artificial/convexe_identifie.arff
--- a/artificial/convexe_mal_separe.arff
+++ b/artificial/convexe_mal_separe.arff
--- a/artificial/cuboids.arff
+++ b/artificial/cuboids.arff
--- a/artificial/cure-t0-2000n-2D.arff
+++ b/artificial/cure-t0-2000n-2D.arff
--- a/artificial/cure-t1-2000n-2D.arff
+++ b/artificial/cure-t1-2000n-2D.arff
--- a/artificial/cure-t2-4k.arff
+++ b/artificial/cure-t2-4k.arff
--- a/artificial/curves1.arff
+++ b/artificial/curves1.arff
--- a/artificial/curves2.arff
+++ b/artificial/curves2.arff
--- a/artificial/dartboard1.arff
+++ b/artificial/dartboard1.arff
--- a/artificial/dartboard2.arff
+++ b/artificial/dartboard2.arff
--- a/artificial/dense-disk-3000.arff
+++ b/artificial/dense-disk-3000.arff
--- a/artificial/dense-disk-5000.arff
+++ b/artificial/dense-disk-5000.arff
--- a/artificial/densite_variable.arff
+++ b/artificial/densite_variable.arff
--- a/artificial/diamond.arff
+++ b/artificial/diamond.arff
--- a/artificial/diamond9.arff
+++ b/artificial/diamond9.arff
--- a/artificial/disk-1000n.arff
+++ b/artificial/disk-1000n.arff
--- a/artificial/disk-3000n.arff
+++ b/artificial/disk-3000n.arff
--- a/artificial/disk-4000n.arff
+++ b/artificial/disk-4000n.arff
--- a/artificial/disk-4500n.arff
+++ b/artificial/disk-4500n.arff
--- a/artificial/disk-4600n.arff
+++ b/artificial/disk-4600n.arff
--- a/artificial/disk-5000n.arff
+++ b/artificial/disk-5000n.arff
--- a/artificial/disk-6000n.arff
+++ b/artificial/disk-6000n.arff
--- a/artificial/donut1.arff
+++ b/artificial/donut1.arff
--- a/artificial/donut2.arff
+++ b/artificial/donut2.arff
--- a/artificial/donut3.arff
+++ b/artificial/donut3.arff
--- a/artificial/donutcurves.arff
+++ b/artificial/donutcurves.arff
--- a/artificial/dpb.arff
+++ b/artificial/dpb.arff
--- a/artificial/dpc.arff
+++ b/artificial/dpc.arff
--- a/artificial/ds2c2sc13.arff
+++ b/artificial/ds2c2sc13.arff
@ -0,0 +1,599 @@
+@RELATION ds2c2sc13
+%
+% Katti Faceli, Tiemi C. Sakata, Marcilio C. P. de Souto, and Andr\&\#233; C. P. L. F. de Carvalho. 2010. 
+% Partitions selection strategy for set of clustering solutions. Neurocomput. 73, 16-18 (October 2010), 
+% 2809-2819. DOI=10.1016/j.neucom.2010.03.028 http://dx.doi.org/10.1016/j.neucom.2010.03.028
+%
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--- a/artificial/ds3c3sc6.arff
+++ b/artificial/ds3c3sc6.arff
@ -0,0 +1,916 @@
+@RELATION ds3c3sc6
+%
+% Katti Faceli, Tiemi C. Sakata, Marcilio C. P. de Souto, and Andr\&\#233; C. P. L. F. de Carvalho. 2010. 
+% Partitions selection strategy for set of clustering solutions. Neurocomput. 73, 16-18 (October 2010), 
+% 2809-2819. DOI=10.1016/j.neucom.2010.03.028 http://dx.doi.org/10.1016/j.neucom.2010.03.028
+%
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+@ATTRIBUTE y REAL
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--- a/artificial/ds4c2sc8.arff
+++ b/artificial/ds4c2sc8.arff
@ -0,0 +1,496 @@
+@RELATION ds4c2sc8
+%
+% Katti Faceli, Tiemi C. Sakata, Marcilio C. P. de Souto, and Andr\&\#233; C. P. L. F. de Carvalho. 2010. 
+% Partitions selection strategy for set of clustering solutions. Neurocomput. 73, 16-18 (October 2010), 
+% 2809-2819. DOI=10.1016/j.neucom.2010.03.028 http://dx.doi.org/10.1016/j.neucom.2010.03.028
+%
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE class {1,2,3,4,5,6,7,8}
+
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--- a/artificial/elliptical_10_2.arff
+++ b/artificial/elliptical_10_2.arff
@ -0,0 +1,523 @@
+% Data_10_2 or AD_10_2: This data is in 2-d space, and has 10 clusters.  The total number of  points is 500.
+%
+% If you use this data, please quote the following references:
+%
+% S. Bandyopadhyay and U. Maulik, ``Nonparametric genetic clustering: Comparison validity indices'',
+% IEEE Transactions on Systems, Man and Cybernetics, Part C, vol. 31, no. 1, pp. 120-125, 2001
+%
+% S. Bandyopadhyay and U. Maulik, ``Genetic Clustering for Automatic Evolution
+% of Clusters and Application to Image Classification'',  Pattern Recognition, vol.35,
+% pp. 1197-1208, 2002
+%
+% S. Bandyopadhyay and S. K. Pal, ``Classification and Learning Using Genetic
+% Algorithms: Applications in Bioinformatics and Web Intelligence", Springer,
+% Heidelberg, 2007
+% 
+% source: http://www.isical.ac.in/~sanghami/data.html
+@RELATION elliptical_10_2
+
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE class {0,1,2,3,4,5,6,7,8,9}
+
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--- a/artificial/elly-2d10c13s.arff
+++ b/artificial/elly-2d10c13s.arff
--- a/artificial/elly.arff
+++ b/artificial/elly.arff
--- a/artificial/engytime.arff
+++ b/artificial/engytime.arff
--- a/artificial/flame.arff
+++ b/artificial/flame.arff
@ -0,0 +1,250 @@
+% Flame, N=240, M=2, D=2
+% Fu, L. and E. Medico, FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC bioinformatics, 2007. 8(1): p. 3.
+%
+@RELATION flame
+
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
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+
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--- a/artificial/fourty.arff
+++ b/artificial/fourty.arff
--- a/artificial/gaussians1.arff
+++ b/artificial/gaussians1.arff
@ -0,0 +1,110 @@
+@relation gaussians1
+% %% generated two Gaussian clouds (R lang)
+% cl1 <- cbind(rnorm(100,0.2,0.05),rnorm(100,0.8,0.06))
+% cl2 <- cbind(rnorm(50,0.7,0.08),rnorm(50,0.3,0.05))
+%
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE class {1,2}
+
+@data
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--- a/artificial/golfball.arff
+++ b/artificial/golfball.arff
--- a/artificial/hepta.arff
+++ b/artificial/hepta.arff
@ -0,0 +1,232 @@
+% This Fundamental Clustering Problems Suite (FCPS) may serve as a minimal test for new invented cluster algorithms.
+%
+% Every new invented algorithm should at least cluster the FCPS correctly.
+%
+% The data sets may be used as long as the following citation is referenced in any publication
+% concering any of this data:
+%
+% Alfred Ultsch, Clustering with SOM: U*C, in Proc. Workshop on Self Organizing Feature Maps ,pp 31-37 Paris 2005.
+%
+% main problem: none, i.e clear defined clusters
+%
+@relation Hepta
+
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--- a/artificial/hypercube.arff
+++ b/artificial/hypercube.arff
@ -0,0 +1,818 @@
+% Data generated using R package 'mlbench'
+%
+% > library("mlbench") 
+% > set.seed(1234)
+% > data <- mlbench.hypercube(1000)
+%
+% The created data are \code{d}-dimensional spherical Gaussians with standard
+% deviation \code{sd} and means at the corners of a
+% \code{d}-dimensional hypercube. The number of classes is \eqn{2^d}.
+%
+@relation hypercube
+
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+
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--- a/artificial/impossible.arff
+++ b/artificial/impossible.arff
--- a/artificial/insect.arff
+++ b/artificial/insect.arff
@ -0,0 +1,41 @@
+% source: http://people.sc.fsu.edu/~jburkardt/datasets/martinez/martinez.html
+% The INSECT data set contains 10 measurements (rows) of 3 quantities (columns) for each of 3 species of insect. This data can be used to test clustering techniques.
+%
+@relation insect
+
+@attribute attr1 numeric
+@attribute attr2 numeric
+@attribute attr3 numeric
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+
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--- a/artificial/jain.arff
+++ b/artificial/jain.arff
@ -0,0 +1,377 @@
+% Jain
+% N=373, M=2, D=2
+% Jain, A. and M. Law, Data clustering: A user's dilemma. Lecture Notes in Computer Science, 2005. 3776: p. 1-10.
+%
+@RELATION jain
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--- a/artificial/long1.arff
+++ b/artificial/long1.arff
--- a/artificial/long2.arff
+++ b/artificial/long2.arff
--- a/artificial/long3.arff
+++ b/artificial/long3.arff
--- a/artificial/longsquare.arff
+++ b/artificial/longsquare.arff
@ -0,0 +1,909 @@
+% Handl, Julia, and Joshua Knowles. "Multiobjective clustering with automatic determination of the number of clusters."
+% UMIST, Manchester, Tech. Rep. TR-COMPSYSBIO-2004-02 (2004).
+@RELATION longsquare
+%
+@ATTRIBUTE a0 REAL
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+%
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--- a/artificial/losange.arff
+++ b/artificial/losange.arff
--- a/artificial/lsun.arff
+++ b/artificial/lsun.arff
@ -0,0 +1,419 @@
+% This Fundamental Clustering Problems Suite (FCPS) may serve as a minimal test for new invented cluster algorithms.
+%
+% Every new invented algorithm should at least cluster the FCPS correctly.
+%
+% The data sets may be used as long as the following citation is referenced in any publication
+% concering any of this data:
+%
+% Alfred Ultsch, Clustering with SOM: U*C, in Proc. Workshop on Self Organizing Feature Maps ,pp 31-37 Paris 2005.
+%
+% main problem: different variances and inter cluster distances
+%
+@relation lsun
+
+@ATTRIBUTE x REAL
+@ATTRIBUTE y REAL
+@ATTRIBUTE class {1,2,3}
+
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+
--- a/artificial/mopsi-finland.arff
+++ b/artificial/mopsi-finland.arff
--- a/artificial/mopsi-joensuu.arff
+++ b/artificial/mopsi-joensuu.arff
--- a/artificial/non_convexe.arff
+++ b/artificial/non_convexe.arff
--- a/artificial/pathbased.arff
+++ b/artificial/pathbased.arff
@ -0,0 +1,313 @@
+% Pathbased dataset, N=300, M=3, D=2
+%
+% Chang, H. and D.Y. Yeung, Robust path-based spectral clustering. Pattern Recognition, 2008. 41(1): p. 191-203.
+%
+% source: http://cs.joensuu.fi/sipu/datasets/
+
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--- a/artificial/pmf.arff
+++ b/artificial/pmf.arff
@ -0,0 +1,657 @@
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+
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--- a/artificial/rectangles.arff
+++ b/artificial/rectangles.arff
--- a/artificial/rings.arff
+++ b/artificial/rings.arff
--- a/artificial/s-set1.arff
+++ b/artificial/s-set1.arff
--- a/artificial/s-set2.arff
+++ b/artificial/s-set2.arff
--- a/artificial/s-set3.arff
+++ b/artificial/s-set3.arff
--- a/artificial/s-set4.arff
+++ b/artificial/s-set4.arff
--- a/artificial/shapes.arff
+++ b/artificial/shapes.arff
--- a/Show more
+++ b/Show more