聚类分析 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "聚类分析" in Chinese language version.

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7doi.org

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3semanticscholar.org

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3web.archive.org

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3worldcat.org

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2termonline.cn

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2naer.edu.tw

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2psu.edu

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2archive.org

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1microsoft.com

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archive.org

Feldman, Ronen; Sanger, James. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge Univ. Press. 2007-01-01. ISBN 978-0521836579. OCLC 915286380.
Weiss, Sholom M.; Indurkhya, Nitin; Zhang, Tong; Damerau, Fred J. Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer. 2005. ISBN 978-0387954332. OCLC 803401334.

doi.org

Estivill-Castro, Vladimir. Why so many clustering algorithms – A Position Paper. ACM SIGKDD Explorations Newsletter. 20 June 2002, 4 (1): 65–75. S2CID 7329935. doi:10.1145/568574.568575.
Sibson, R. SLINK: an optimally efficient algorithm for the single-link cluster method (PDF). The Computer Journal (British Computer Society). 1973, 16 (1): 30–34 [2022-12-06]. doi:10.1093/comjnl/16.1.30. （原始内容存档 (PDF)于2014-09-24）.
Defays, D. An efficient algorithm for a complete link method. The Computer Journal (British Computer Society). 1977, 20 (4): 364–366. doi:10.1093/comjnl/20.4.364.
Lloyd, S. Least squares quantization in PCM. IEEE Transactions on Information Theory. 1982, 28 (2): 129–137. doi:10.1109/TIT.1982.1056489.
Kriegel, Hans-Peter; Kröger, Peer; Sander, Jörg; Zimek, Arthur. Density-based Clustering. WIREs Data Mining and Knowledge Discovery. 2011, 1 (3): 231–240 [2022-12-06]. S2CID 36920706. doi:10.1002/widm.30. （原始内容存档于2016-11-17）.
Achtert, E.; Böhm, C.; Kröger, P. Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science 3918: 119–128. 2006. CiteSeerX 10.1.1.64.1161 . ISBN 978-3-540-33206-0. doi:10.1007/11731139_16. |contribution=被忽略 (帮助)
Pfitzner, Darius; Leibbrandt, Richard; Powers, David. Characterization and evaluation of similarity measures for pairs of clusterings. Knowledge and Information Systems (Springer). 2009, 19 (3): 361–394. S2CID 6935380. doi:10.1007/s10115-008-0150-6.

gsu.edu

cs.gsu.edu

Sibson, R. SLINK: an optimally efficient algorithm for the single-link cluster method (PDF). The Computer Journal (British Computer Society). 1973, 16 (1): 30–34 [2022-12-06]. doi:10.1093/comjnl/16.1.30. （原始内容存档 (PDF)于2014-09-24）.

microsoft.com

academic.research.microsoft.com

Microsoft academic search: most cited data mining articles 互联网档案馆的存檔，存档日期2010-04-21.: DBSCAN is on rank 24, when accessed on: 4/18/2010

naer.edu.tw

terms.naer.edu.tw

聚類分析. 樂詞網. 國家教育研究院（中文（臺灣））.
集群分析. 樂詞網. 國家教育研究院（中文（臺灣））.

psu.edu

citeseerx.ist.psu.edu

Ankerst, Mihael; Breunig, Markus M.; Kriegel, Hans-Peter; Sander, Jörg. OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD international conference on Management of data. ACM Press: 49–60. 1999. CiteSeerX 10.1.1.129.6542 .
Achtert, E.; Böhm, C.; Kröger, P. Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science 3918: 119–128. 2006. CiteSeerX 10.1.1.64.1161 . ISBN 978-3-540-33206-0. doi:10.1007/11731139_16. |contribution=被忽略 (帮助)

semanticscholar.org

api.semanticscholar.org

Estivill-Castro, Vladimir. Why so many clustering algorithms – A Position Paper. ACM SIGKDD Explorations Newsletter. 20 June 2002, 4 (1): 65–75. S2CID 7329935. doi:10.1145/568574.568575.
Kriegel, Hans-Peter; Kröger, Peer; Sander, Jörg; Zimek, Arthur. Density-based Clustering. WIREs Data Mining and Knowledge Discovery. 2011, 1 (3): 231–240 [2022-12-06]. S2CID 36920706. doi:10.1002/widm.30. （原始内容存档于2016-11-17）.
Pfitzner, Darius; Leibbrandt, Richard; Powers, David. Characterization and evaluation of similarity measures for pairs of clusterings. Knowledge and Information Systems (Springer). 2009, 19 (3): 361–394. S2CID 6935380. doi:10.1007/s10115-008-0150-6.

termonline.cn

聚类分析. 术语在线. 全国科学技术名词审定委员会. （简体中文）
聚类. 术语在线. 全国科学技术名词审定委员会. （简体中文）

web.archive.org

Sibson, R. SLINK: an optimally efficient algorithm for the single-link cluster method (PDF). The Computer Journal (British Computer Society). 1973, 16 (1): 30–34 [2022-12-06]. doi:10.1093/comjnl/16.1.30. （原始内容存档 (PDF)于2014-09-24）.
Kriegel, Hans-Peter; Kröger, Peer; Sander, Jörg; Zimek, Arthur. Density-based Clustering. WIREs Data Mining and Knowledge Discovery. 2011, 1 (3): 231–240 [2022-12-06]. S2CID 36920706. doi:10.1002/widm.30. （原始内容存档于2016-11-17）.
Microsoft academic search: most cited data mining articles 互联网档案馆的存檔，存档日期2010-04-21.: DBSCAN is on rank 24, when accessed on: 4/18/2010

wiley.com

wires.wiley.com

Kriegel, Hans-Peter; Kröger, Peer; Sander, Jörg; Zimek, Arthur. Density-based Clustering. WIREs Data Mining and Knowledge Discovery. 2011, 1 (3): 231–240 [2022-12-06]. S2CID 36920706. doi:10.1002/widm.30. （原始内容存档于2016-11-17）.

worldcat.org

Aggarwal, Charu C.; Reddy, Chandan K. (编). Data Clustering : Algorithms and Applications. ISBN 978-1-315-37351-5. OCLC 1110589522.
Feldman, Ronen; Sanger, James. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge Univ. Press. 2007-01-01. ISBN 978-0521836579. OCLC 915286380.
Weiss, Sholom M.; Indurkhya, Nitin; Zhang, Tong; Damerau, Fred J. Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer. 2005. ISBN 978-0387954332. OCLC 803401334.