A Large Benchmark Dataset for Web Document Clustering
Mark P. Sinka, David Corne · 2002
Targeting useful and relevant information on the WWW is a topical and highly complicated research area. A thriving research effort that feeds into this area is document clustering, which overlaps closely with areas usually known as text classification and text categorisation. A foundational aspect of such research (which has been proven over and over again in other research disciplines) is the use of standard datasets, against which different techniques can be properly benchmarked andassessedincomparisontoeachother. Wenotehereinthat,sofarinthisbroad area of research, as many datasets have been used as research papers written, thus making it difficult to reason about the relative performance of different categorisation/clustering techniques used in different papers. In this paper we propose a standard dataset with a variety of properties suitable for a wide range of clustering and related experiments. We describe how the dataset was generated, and provide a pointer to it, and encourage its access and use. We also illustrate the use of part of the dataset by establishing benchmark results for simple k-means clustering, comparing the relative performance of k-means on a pair of `close' categories and a pair of `distant' categories. We naturally find that performance is better on the pair of `distant' categories, however the experiments reveal that although stop-word removal is confirmed as helpful, word-stemming is, (perhaps counter to intuition), not necessarily always recommended on `distant' categories.