Description Usage Arguments Value Author(s) References See Also Examples
Performing Modal Clustering
1 2 3 4 |
dat |
Matrix of data points |
length |
number of smoothing levels. Default is 10 |
sigmaselect |
Specified Smoothing levels. Default NULL will calculate the Sigma levels using concept of spectral degrees of freedom given in Lindsay et al (2008) |
npart |
Number of random partitions when using parallel computing. If using several processors of a machine one option is to choose the number of partitions equal to the number of processors |
parallel |
If TRUE uses parallel comptation using |
G |
Specified values of modes. A matrix with number or rows equal to the number of modes and number of columns equal to the dimension of the data. Defualt value is NULL |
data |
Same as the input Data |
n.cluster |
Number of clusters at each level. |
level |
Levels corresponding to each smoothing parameter. |
sigmas |
Same as input sigmaselect if provided or dynamically calculated smoothing levels based on Spectral Degrees of Freedom criterion. Uses the function khat.inv |
mode |
List of modes at each distinct levels. |
membership |
List of memmbership to modes at each distinct levels. |
Surajit Ray and Yansong Cheng
Li. J, Ray. S, Lindsay. B. G, "A nonparametric statistical approach to clustering via mode identification," Journal of Machine Learning Research , 8(8):1687-1723, 2007.
Lindsay, B.G., Markatou M., Ray, S., Yang, K., Chen, S.C. "Quadratic distances on probabilities: the foundations," The Annals of Statistics Vol. 36, No. 2, page 983–1006, 2008.
soft.hmac
for soft clustering at specified levels.
hard.hmac
for hard clustering at specified levels.
See plot.hmac
.
1 2 3 4 5 6 7 8 9 10 11 12 13 | data(disc2d)
## Not run: disc2d.hmac=phmac(disc2d,npart=1)
plot.hmac(disc2d.hmac,level=2)
## For parallel implementation
## Not run: disc2d.hmac.parallel=phmac(disc2d,npart=2,parallel=TRUE)
soft.hmac(disc2d.hmac,level=2)
soft.hmac(disc2d.hmac,n.cluster=3)
hard.hmac(disc2d.hmac,n.cluster=3)
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