Text Data Clustering by Contextual Graphs

In this paper, we focus on the class of graph-based clustering models, such as growing neural gas or idiotypic nets for the purpose of high-dimensional text data clustering. We present a novel approach, which does not require operation on the complex overall graph of clusters, but rather allows to shift majority of effort to context-sensitive, local subgraph and local sub-space processing. Savings of orders of magnitude in processing time and memory can be achieved, while the quality of clusters is improved, as presented experiments demonstrate.