robust_init_density: Robust initialization based on inverse density estimator
In anevolbap/ktaucenterscpp: Robust Clustering Procedures

Description Usage Arguments Details Value Note Author(s) References See Also Examples

robust_init_density searches for k initial cluster seeds for k-means-based clustering methods.

1	robust_init_density(dist_matrix, data, n_clusters, mp = 10, method = "density")

`dist_matrix`	A distance matrix calculated on `data`.
`data`	A data matrix with n rows and p columns.
`mp`	The number of the nearest neighbors to find dense regions by LOF, the default is 10.
`k`	The number of cluster centers to find.

This function do the same as ROBIN but taking into account the density (instead of the inverse of the average relative local density known as LOF)

The centers are the observations located in the most dense region and far away from each other at the same time. In order to find the observations in the highly dense region, ROBINPOINTDEN uses point density estimation (instead of Local Outlier Factor, Breunig et al. (2000)), see more details.

Observation: Outliers have a high 'idp' value. In imbalanced cases and when K increases, all the observations from a group might be above the critRobin, So we need to increase the critRobin in order to avoid two initials centers from the same group. modification: start with a point whose density is maximum

`centers`	A numeric vector of `k` initial cluster centers corresponding to the k indices of observations.
`idpoints`	A real vector containing the inverse density values of each point (observation).

this is a slightly modified version of ROBIN algorithm implementation done by Sarka Brodinova <sarka.brodinova@tuwien.ac.at>.

Juan Domingo Gonzalez <juanrst@hotmail.com>

Hasan AM, et al. Robust partitional clustering by outlier and density insensitive seeding. Pattern Recognition Letters, 30(11), 994-1002, 2009.

lof

K=5;
nk=100
Z <- rnorm(2 * K * nk);
centers_aux <- -floor(K/2):floor(K/2)
mues <- rep(5*centers_aux,2*nk*K )
X <-  matrix(Z + mues, ncol=2)
# Generate synthetic outliers (contamination level 20%)
X[sample(1:(nk * K),(nk * K) * 0.2), ] <-matrix(runif((nk * K) * 0.2 * 2,
                                          3 * min(X), 3 * max(X)),
                                          ncol = 2, nrow = (nk * K) * 0.2)
res <- robust_init_density(dist_matrix =dist(X), data=X, k = K);
# plot the Initial centers found
plot(X)
points(X[res$centers,],pch=19,col=4,cex=2)