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Titlebook: Advances in Knowledge Discovery and Data Mining; 17th Pacific-Asia Co Jian Pei,Vincent S. Tseng,Guandong Xu Conference proceedings 2013 Spr

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樓主: legerdemain
31#
發(fā)表于 2025-3-26 23:49:49 | 只看該作者
32#
發(fā)表于 2025-3-27 01:47:54 | 只看該作者
33#
發(fā)表于 2025-3-27 06:46:00 | 只看該作者
Conference proceedings 2013ng, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, appl
34#
發(fā)表于 2025-3-27 11:19:37 | 只看該作者
Conference proceedings 2013ications, machine learning, feature selection and dimensionality reduction, multiple information sources mining, social networks, clustering, text mining, text classification, imbalanced data, privacy-preserving data mining, recommendation, multimedia data mining, stream data mining, data preprocessing and representation.
35#
發(fā)表于 2025-3-27 17:02:59 | 只看該作者
Cardiac Complications of Malignancyerns from the tree. Experimental results show that (i)?our tree is usually more compact than the UF-tree or UFP-tree, (ii)?our tree can be as compact as the FP-tree, and (iii)?our mining algorithm finds frequent patterns efficiently.
36#
發(fā)表于 2025-3-27 18:15:55 | 只看該作者
Renal Complications of Malignancyanking fraud detection show that the ConvergMiner greatly outperforms the existing cost-sensitive classification methods in terms of predicative accuracy. In particular, the efficiency improves with the increase of data imbalance.
37#
發(fā)表于 2025-3-28 01:29:45 | 只看該作者
Alon Barsheshet MD,Ilan Goldenberg MDs which capture the representative usage behaviors of appliances in a smart home environment and (ii) the corresponding algorithms for discovering usage patterns efficiently. Finally, we apply our algorithms on a real-world dataset to show the practicability of usage pattern mining.
38#
發(fā)表于 2025-3-28 02:17:20 | 只看該作者
Atrioventricular Nodal Reentry Tachycardiare additional data access. Both reduce A2DE’s learning bias, improving its effectiveness for big data. Furthermore, we demonstrate that the techniques are complementary. The resulting combined technique delivers computationally efficient low-bias learning well suited to learning from big data.
39#
發(fā)表于 2025-3-28 08:07:55 | 只看該作者
40#
發(fā)表于 2025-3-28 11:14:12 | 只看該作者
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