{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:54:48Z","timestamp":1754157288511,"version":"3.41.2"},"reference-count":25,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2008,4,11]],"date-time":"2008-04-11T00:00:00Z","timestamp":1207872000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,4,11]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to examine neural document clustering techniques, e.g. self\u2010organising map (SOM) or growing neural gas (GNG), usually assume that textual information is stationary on the quantity.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The authors propose a novel dynamic adaptive self\u2010organising hybrid (DASH) model, which adapts to time\u2010event news collections not only to the neural topological structure but also to its main parameters in a non\u2010stationary environment. Based on features of a time\u2010event news collection in a non\u2010stationary environment, they review the main current neural clustering models. The main deficiency is a need of pre\u2010definition of the thresholds of unit\u2010growing and unit\u2010pruning. Thus, the dynamic adaptive self\u2010organising hybrid (DASH) model is designed for a non\u2010stationary environment.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The paper compares DASH with SOM and GNG based on an artificial jumping corner data set and a real world Reuters news collection. According to the experimental results, the DASH model is more effective than SOM and GNG for time\u2010event document clustering.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>A real world environment is dynamic. This paper provides an approach to present news clustering in a non\u2010stationary environment.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>Text clustering in a non\u2010stationary environment is a novel concept. The paper demonstrates DASH, which can deal with a real world data set in a non\u2010stationary environment.<\/jats:p><\/jats:sec>","DOI":"10.1108\/02640470810864145","type":"journal-article","created":{"date-parts":[[2008,4,12]],"date-time":"2008-04-12T07:08:20Z","timestamp":1207984100000},"page":"260-272","source":"Crossref","is-referenced-by-count":2,"title":["A novel self\u2010organising clustering model for time\u2010event documents"],"prefix":"10.1108","volume":"26","author":[{"given":"Chihli","family":"Hung","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Wermter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2022020119461017400_b1","unstructured":"Ahrns, I., Bruske, J. and Sommer, G. (1995), \u201cOn\u2010line learning with dynamic cell structures\u201d, Proceedings of ICANN\u201095, the International Conference on Artificial Neural Networks, pp. 141\u20106."},{"key":"key2022020119461017400_b2","unstructured":"Blackmore, J. and Miikkulainen, R. (1993), \u201cIncremental grid growing: encoding high\u2010dimensional structure into a two\u2010dimensional feature map\u201d, Proceedings of the IEEE International Conference on Neural Networks (ICNN'93)."},{"key":"key2022020119461017400_b3","doi-asserted-by":"crossref","unstructured":"Chakrabarti, S. (2000), \u201cData mining for hypertext: a tutorial survey\u201d, ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Explorations, Vol. 1 No. 2, pp. 1\u201011.","DOI":"10.1145\/846183.846187"},{"key":"key2022020119461017400_b4","doi-asserted-by":"crossref","unstructured":"Chang, C.\u2010C. and Chen, R.\u2010S. (2006), \u201cUsing data mining technology to solve classification problems \u2013 a case study of campus digital library\u201d, The Electronic Library, Vol. 24 No. 3, pp. 307\u201021.","DOI":"10.1108\/02640470610671178"},{"key":"key2022020119461017400_b5","doi-asserted-by":"crossref","unstructured":"Chen, A.\u2010P. and Chen, C.\u2010C. (2006), \u201cA new efficient approach for data clustering in electronic library using ant colony clustering algorithm\u201d, The Electronic Library, Vol. 24 No. 4, pp. 548\u201059.","DOI":"10.1108\/02640470610689223"},{"key":"key2022020119461017400_b6","doi-asserted-by":"crossref","unstructured":"Fritzke, B. (1994), \u201cGrowing cell structures \u2013 a self\u2010organizing network for unsupervised and supervised learning\u201d, Neural Networks, Vol. 7 No. 9, pp. 1441\u201060.","DOI":"10.1016\/0893-6080(94)90091-4"},{"key":"key2022020119461017400_b7","unstructured":"Fritzke, B. (1995), \u201cA growing neural gas network learns topologies\u201d, in Tesauro, G., Touretzky, D.S. and Leen, T.K. (Eds), Advances in Neural Information Processing Systems 7, Vol. 7, MIT Press, Cambridge MA, pp. 625\u201032."},{"key":"key2022020119461017400_b8","doi-asserted-by":"crossref","unstructured":"Fritzke, B. (1997), \u201cA self\u2010organizing network that can follow non\u2010stationary distributions\u201d, Proceedings of ICANN\u201097, International Conference on Artificial Neural Networks, pp. 613\u201018.","DOI":"10.1007\/BFb0020222"},{"key":"key2022020119461017400_b9","unstructured":"Honkela, T., Kaski, S., Lagus, K. and Kohonen, T. (1997), \u201cWEBSOM\u2010 self\u2010organizing maps of document collections\u201d, Proceedings of Workshop on Self\u2010Organizing Maps 1997 (WSOM'97), Espoo, Finland, pp. 310\u201035."},{"key":"key2022020119461017400_b10","doi-asserted-by":"crossref","unstructured":"Hsu, A.L. and Halgamuge, S.K. (2003), \u201cEnhancement of topology preservation and hierarchical dynamic self\u2010organising maps for data visualization\u201d, International Journal of Approximate Reasoning, Vol. 32 Nos 2\u20103, pp. 259\u201079.","DOI":"10.1016\/S0888-613X(02)00086-5"},{"key":"key2022020119461017400_b11","doi-asserted-by":"crossref","unstructured":"Hung, C., Wermter, S. and Smith, P. (2004), \u201cHybrid neural document clustering using guided selforganisation and WordNet\u201d, IEEE\u2010Intelligent Systems, Vol. 19 No. 2, pp. 68\u201077.","DOI":"10.1109\/MIS.2004.1274914"},{"key":"key2022020119461017400_b12","doi-asserted-by":"crossref","unstructured":"Jain, A.K., Murty, M.N. and Flynn, P.J. (1999), \u201cData clustering: a review\u201d, ACM Computing Surveys, Vol. 31 No. 3, pp. 264\u2010323.","DOI":"10.1145\/331499.331504"},{"key":"key2022020119461017400_b13","unstructured":"Kohonen, T. 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