Online Document Clustering Using the GPU

Online document clustering takes as its input a list of document vectors, ordered by time. A document vector consists of a list of K terms and their associated weights. The generation of terms and their weights from the document text may vary, but the TF-IDF (term frequency-inverse document frequency) method is popular for clustering applications [1]. The assumption is that the resulting document vector is a good overall representation of the original document. We note that the dimensionality of the document vectors is very high (potentially infinite), since a document could potentially contain any word (term). We also note that the vectors are sparse in the sense that most term weights have a zero value. We assume that each term not explicitly present in a particular document vector has a weight of zero. Document vectors are normalized.