  We present a new R package which takes a numerical matrix format as data
input, and computes clusters using a support vector clustering method (SVC). We
have implemented an original 2D-grid labeling approach to speed up cluster
extraction. In this sense, SVC can be seen as an efficient cluster extraction
if clusters are separable in a 2-D map. Secondly we showed that this SVC
approach using a Jaccard-Radial base kernel can help to classify well enough a
set of terms into ontological classes and help to define regular expression
rules for information extraction in documents; our case study concerns a set of
terms and documents about developmental and molecular biology.
