svcR: An R Package for Support Vector Clustering improved with Geometric Hashing applied to Lexical Pattern Discovery

TL;DR

This paper introduces svcR, an R package for support vector clustering that employs a novel 2D-grid labeling technique and a Jaccard-Radial kernel to enhance clustering speed and accuracy in lexical pattern discovery.

Contribution

The paper presents an original 2D-grid labeling method and applies a Jaccard-Radial kernel within SVC to improve clustering efficiency and classification in lexical pattern discovery tasks.

Findings

Enhanced cluster extraction speed with 2D-grid labeling

Effective classification of biological terms into ontological classes

Improved rule definition for information extraction

Abstract

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.