datasets2D: 2D Toy datasets

In ialmodovar/SynClustR: Syncytial Clustering Algorithms in R

2D Toy datasets

Description

Toy datasets to test and demonstrate SynClustR.

Format

Spherical7 is a 2D dimensional dataset of size 500 x 3. The third column contains the true memberships.

Aggregation, 2D dimensional data set of size 788 x 3. The third column contains the true memberships.

Bananas-Arcs, 2D dimensional data set of size 4515 x 3. The third column contains the true memberships.

Bananas-Sphere, 2D dimensional data set of size 3015 x 3. The third column contains the true memberships.

Bananas-Clump, 2D dimensional data set of size 200 x 3. The third column contains the true memberships.

Bullseye is a 2D dimensional dataset of size 400 x 3. The third column contains the true memberships.

Bullseye-Cigarette is a 2D dimensional dataset of size 3025 x 3. The third column contains the true memberships.

Compound is a 2D dimensional dataset of size 788 x 3. The third column contains the true memberships.

D31 is a 2D dimensional dataset of size 3100 x 3. The third column contains the true memberships.

Flame is a 2D dimensional dataset of size 240 x 3. The third column contains the true memberships.

Half.ringed.clusters is a 2D dimensional dataset of size 373 x 3. The third column contains the true memberships.

Pathbased is a 2D dimensional dataset of size 300 x 3. The third column contains the true memberships.

R15 is a 2D dimensional dataset of size 600 x 3. The third column contains the true memberships.

SCX-Bananas is a 2D dimensional dataset of size 3420 x 3. The third column contains the true memberships.

Spiral is a 2D dimensional dataset of size 312 x 3. The third column contains the true memberships.

SSS is a 2D dimensional dataset of size 5015 x 3. The third column contains the true memberships.

XXXX is a 2D dimensional dataset of size 415 x 3. The third column contains the true memberships.

Details

Spherical7 is a 2D dimensional dataset of size 500 x 3. The third column contains the true memberships.

Aggregation, 2D dimensional data set of size 788 x 3. The third column contains the true memberships.

Bananas-Arcs, 2D dimensional data set of size 4515 x 3. The third column contains the true memberships.

Bananas-Sphere, 2D dimensional data set of size 3015 x 3. The third column contains the true memberships.

Bananas-Clump, 2D dimensional data set of size 200 x 3. The third column contains the true memberships.

Bullseye is a 2D dimensional dataset of size 400 x 3. The third column contains the true memberships.

Bullseye-Cigarette is a 2D dimensional dataset of size 3025 x 3. The third column contains the true memberships.

Compound is a 2D dimensional dataset of size 788 x 3. The third column contains the true memberships.

D31 is a 2D dimensional dataset of size 3100 x 3. The third column contains the true memberships.

Flame is a 2D dimensional dataset of size 240 x 3. The third column contains the true memberships.

Half.ringed.clusters is a 2D dimensional dataset of size 373 x 3. The third column contains the true memberships.

Pathbased is a 2D dimensional dataset of size 300 x 3. The third column contains the true memberships.

R15 is a 2D dimensional dataset of size 600 x 3. The third column contains the true memberships.

SCX-Bananas is a 2D dimensional dataset of size 3420 x 3. The third column contains the true memberships.

Spiral is a 2D dimensional dataset of size 312 x 3. The third column contains the true memberships.

SSS is a 2D dimensional dataset of size 5015 x 3. The third column contains the true memberships.

XXXX is a 2D dimensional dataset of size 415 x 3. The third column contains the true memberships.

Author(s)

Israel A. Almodovar-Rivera and Ranjan Maitra

References

Almodovar-Rivera, I., & Maitra, R. (2018). Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering. arXiv preprint arXiv:1805.09505.

H. Chang and D.-Y. Yeung (2008). "Robust path-based spectral clustering." Pattern Recognition, 41(1):191-203. ISSN 0031-3203.

L. Fu and E. Medico, FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC bioinformatics, 2007. 8(1): p. 3.

A. Gionis, H. Mannila, and P. Tsaparas, Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 2007. 1(1): p. 1-30.

A. Jain and M. Law, Data clustering: A user's dilemma. Lecture Notes in Computer Science, 2005. 3776: p. 1-10.

Peterson, A. D., Ghosh, A. P., & Maitra, R. (2018). Merging K-means with hierarchical clustering for identifying general-shaped groups. Stat, 7(1), e172.

W. Stuetzle and R. Nugent, "A generalized single linkage method for estimating the cluster tree of a density," Journal of Computational and Graphical Statistics, 2010.

C. J. Veenman, M. J. T. Reinders, and E. Backer (2002). "A maximum variance cluster algorithm." IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (9):1273-1280.

C.T. Zahn, Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers, 1971. 100(1): p. 68-86.

Examples

##
par(mfrow=c(3,4),mar=rep(1.75,4))
data("Spherical7")
with(Spherical7,plot(x,y,col=Ids,main="7-Spherical",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("Bananas-Arcs")
with(Bananas.Arcs,plot(x,y,col=Ids,main="Bananas-Arcs",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("Banana-Clump")
with(Banana.Clump,plot(x,y,col=Ids,main="Banana-Clump",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("Bullseye")
with(Bullseye,plot(x,y,col=Ids,main="Bullseye",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("Bullseye-Cigarette")
with(Bullseye.Cigarette,plot(x,y,col=Ids,main="Bullseye-Cigarette",xlab = "",ylab = "",
xlim=c( 0.6575804,11.7114793),axes = FALSE,frame.plot = TRUE))

data("Compound")
with(Compound,plot(x,y,col=Ids,main="Compound",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))


data("Half-ringed-clusters")
with(Half.ringed.clusters,plot(x,y,col=Ids,main="Half-ringed-clusters",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("Path-based")
with(Pathbased,plot(x,y,col=Ids,main="Path-based",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("SCX-Bananas")
with(SCX.Bananas,plot(x,y,col=Ids,main="SCX-Bananas",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("Spiral")
with(Spiral,plot(x,y,col=Ids,main="Spiral",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("SSS")
with(SSS,plot(x,y,col=Ids,main="SSS",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

data("XXXX")
with(XXXX,plot(x,y,col=Ids,main="XXXX",
xlab = "",ylab = "",axes = FALSE,frame.plot = TRUE))

ialmodovar/SynClustR documentation built on July 7, 2023, 12:18 a.m.