# hybrid.kmeans

##### Hybrid K-means

Hybrid K-means clustering using hierarchical clustering to define cluster-centers

##### Usage

`hybrid.kmeans(x, k = 2, hmethod = "ward.D", stat = mean, ...)`

##### Arguments

- x
A data.frame or matrix with data to be clustered

- k
Number of clusters

- hmethod
The agglomeration method used in hclust

- stat
The statistic to aggregate class centers (mean or median)

- ...
Additional arguments passed to

`kmeans`

##### Details

This method uses hierarchical clustering to define the cluster-centers in the K-means clustering algorithm. This mitigates some of the know convergence issues in K-means.

##### Value

returns an object of class "kmeans" which has a print and a fitted method

##### Note

options for hmethod are: "ward.D", "ward.D2", "single", "complete", "average", mcquitty", "median", "centroid"

##### References

Singh, H., & K. Kaur (2013) New Method for Finding Initial Cluster Centroids in K-means Algorithm. International Journal of Computer Application. 74(6):27-30

Ward, J.H., (1963) Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association. 58:236-24

##### See Also

`kmeans`

for available ... arguments and function details

`hclust`

for details on hierarchical clustering

##### Examples

```
# NOT RUN {
x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
# Compare k-means to hybrid k-means with k=4
km <- kmeans(x, 4)
hkm <- hybrid.kmeans(x,k=4)
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(x[,1],x[,2], col=km$cluster,pch=19, main="K-means")
plot(x[,1],x[,2], col=hkm$cluster,pch=19, main="Hybrid K-means")
par(opar)
# }
```

*Documentation reproduced from package spatialEco, version 1.3-2, License: GPL-3*