An investigation into the stability of contextual document clustering Export

@article{Rooney_JASIST_2008, abstract = {In this article, we assess the effectiveness of Contextual Document Clustering (CDC) as a means of indexing within a dynamic and rapidly changing environment. We simulate a dynamic environment, by splitting two chronologically ordered datasets into time-ordered segments and assessing how the technique performs under two different scenarios. The first is when new documents are added incrementally without reclustering [incremental CDC (iCDC)], and the second is when reclustering is performed [nonincremental CDC (nCDC)]. The datasets are very large, are independent of each other, and belong to two very different domains. We show that CDC itself is effective at clustering very large document corpora, and that, significantly, it lends itself to a very simple, efficient incremental document addition process that is seen to be very stable over time despite the size of the corpus growing considerably. It was seen to be effective at incrementally clustering new documents even when the corpus grew to six times its original size. This is in contrast to what other researchers have found when applying similar simple incremental approaches to document clustering. The stability of iCDC is accounted for by the unique manner in which CDC discovers cluster themes.}, address = {Northern Ireland Knowledge Engineering Laboratory, University of Ulster, Jordanstown, Newtownabbey BT37 OQB, United Kingdom; Faculty of Applied Mathematics \& Control Processes, St. Petersburg State University, 35 University Ave., St. Petersburg 198504, Russia}, author = {Rooney, Niall and Patterson, David and Galushka, Mykola and Dobrynin, Vladimir and Smirnova, Elena}, citeulike-article-id = {2629606}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/asi.20740}, citeulike-linkout-1 = {http://www3.interscience.wiley.com/cgi-bin/abstract/116835813/ABSTRACT}, doi = {10.1002/asi.20740}, journal = {Journal of the American Society for Information Science and Technology}, keywords = {clustering, contextual, ir}, number = {2}, pages = {256--266}, posted-at = {2008-04-04 14:04:43}, priority = {2}, title = {An investigation into the stability of contextual document clustering}, url = {http://dx.doi.org/10.1002/asi.20740}, volume = {59}, year = {2008} }

TY - JOUR ID - Rooney_JASIST_2008 L3 - citeulike-article-id:2629606 N2 - In this article, we assess the effectiveness of Contextual Document Clustering (CDC) as a means of indexing within a dynamic and rapidly changing environment. We simulate a dynamic environment, by splitting two chronologically ordered datasets into time-ordered segments and assessing how the technique performs under two different scenarios. The first is when new documents are added incrementally without reclustering [incremental CDC (iCDC)], and the second is when reclustering is performed [nonincremental CDC (nCDC)]. The datasets are very large, are independent of each other, and belong to two very different domains. We show that CDC itself is effective at clustering very large document corpora, and that, significantly, it lends itself to a very simple, efficient incremental document addition process that is seen to be very stable over time despite the size of the corpus growing considerably. It was seen to be effective at incrementally clustering new documents even when the corpus grew to six times its original size. This is in contrast to what other researchers have found when applying similar simple incremental approaches to document clustering. The stability of iCDC is accounted for by the unique manner in which CDC discovers cluster themes. IS - 2 JF - Journal of the American Society for Information Science and Technology AD - Northern Ireland Knowledge Engineering Laboratory, University of Ulster, Jordanstown, Newtownabbey BT37 OQB, United Kingdom; Faculty of Applied Mathematics & Control Processes, St. Petersburg State University, 35 University Ave., St. Petersburg 198504, Russia EP - 266 TI - An investigation into the stability of contextual document clustering VL - 59 SP - 256 KW - clustering KW - contextual KW - ir AU - Rooney, Niall AU - Patterson, David AU - Galushka, Mykola AU - Dobrynin, Vladimir AU - Smirnova, Elena PY - 2008/// UR - http://dx.doi.org/10.1002/asi.20740 ER -