Titlebook: Advances in Knowledge Discovery and Data Mining; 7th Pacific-Asia Con Kyu-Young Whang,Jongwoo Jeon,Jaideep Srivastava Conference proceeding

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書(shū)目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)




書(shū)目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)學(xué)科排名




書(shū)目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Advances in Knowledge Discovery and Data Mining被引頻次




書(shū)目名稱Advances in Knowledge Discovery and Data Mining被引頻次學(xué)科排名




書(shū)目名稱Advances in Knowledge Discovery and Data Mining年度引用




書(shū)目名稱Advances in Knowledge Discovery and Data Mining年度引用學(xué)科排名




書(shū)目名稱Advances in Knowledge Discovery and Data Mining讀者反饋




書(shū)目名稱Advances in Knowledge Discovery and Data Mining讀者反饋學(xué)科排名

Cloudburst

978-3-540-04760-5Springer-Verlag Berlin Heidelberg 2003

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/148644.jpg

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https://doi.org/10.1007/978-3-540-34719-4 needs of these industrial and business specific applications. However, there are still considerable challenges facing this multidisciplinary field. Drawing from some industry specific applications this talk will cover the trends and challenges facing the researchers and practitioners of this rapidl

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constitutional

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https://doi.org/10.1007/978-3-540-34719-4d another variation in this family — K-Harmonic Means (KHM). It has been demonstrated using a small number of “benchmark” datasets that KHM is more robust than K-means and EM. In this paper, we compare their performance statistically. We run K-means, K-Harmonic Means and EM on each of 3600 pairs of

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