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The application of cluster grids significantly reduces the computational cost of ASCCN, and experiments show that ASCCN can efficiently and effectively group arbitrary shaped data points into meaningful clusters.<\/p>","DOI":"10.4018\/jdwm.2010100101","type":"journal-article","created":{"date-parts":[[2011,2,15]],"date-time":"2011-02-15T20:14:43Z","timestamp":1297800883000},"page":"1-15","source":"Crossref","is-referenced-by-count":4,"title":["ASCCN"],"prefix":"10.4018","volume":"6","author":[{"given":"Renxia","family":"Wan","sequence":"first","affiliation":[{"name":"North University for Nationalities and Donghua University, China"}]},{"given":"Lixin","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui Institute of Architecture and Industry, China"}]},{"given":"Xiaoke","family":"Su","sequence":"additional","affiliation":[{"name":"Donghua University, China"}]}],"member":"2432","reference":[{"key":"jdwm.2010100101-0","doi-asserted-by":"crossref","unstructured":"Aggarwal, C. 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In Proceedings of the 24th British national conference on databases (BNCOD\u201907) (pp. 247-258).","DOI":"10.1007\/978-3-540-73390-4_27"},{"key":"jdwm.2010100101-20","doi-asserted-by":"crossref","unstructured":"Li, M., Lee, G., Lee, W., & Sivasubramaniam, A. (2006). PENS: an algorithm for density-based clustering in peer-to-peer systems. In Proceedings of the 1st international conference on scalable information systems.","DOI":"10.1145\/1146847.1146886"},{"key":"jdwm.2010100101-21","unstructured":"MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability (pp. 281-297)."},{"key":"jdwm.2010100101-22","unstructured":"Ng, R. T., & Han, J. (1994). Efficient and effective clustering methods for spatial data mining. 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