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Titlebook: Developing Multi-Database Mining Applications; Animesh Adhikari,Pralhad Ramachandrarao,Witold Ped Book 2010 Springer-Verlag London 2010 Cl

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樓主: adulation
21#
發(fā)表于 2025-3-25 04:55:48 | 只看該作者
Introduction,that possess multiple databases. Global decisions made by such an organization might be more appropriate if they are based on the data distributed over the branches. Moreover, the number of such applications is increasing over time. In this chapter, we discuss some of the major challenges encountere
22#
發(fā)表于 2025-3-25 11:30:15 | 只看該作者
23#
發(fā)表于 2025-3-25 15:43:30 | 只看該作者
Mining Multiple Large Databases,y from multiple databases, it becomes necessary to improve mining multiple databases. In this chapter, we present an idea of multi-database mining by making use of local pattern analysis. We elaborate on the existing specialized and generalized techniques which are used for mining multiple large dat
24#
發(fā)表于 2025-3-25 15:55:36 | 只看該作者
25#
發(fā)表于 2025-3-25 20:46:12 | 只看該作者
26#
發(fā)表于 2025-3-26 01:56:39 | 只看該作者
27#
發(fā)表于 2025-3-26 07:18:36 | 只看該作者
28#
發(fā)表于 2025-3-26 11:22:49 | 只看該作者
Book 2010ss various issues regarding the systematic and efficient development of multi-database mining applications. It explains how systematically one could prepare data warehouses at different branches. As appropriate multi-database mining technique is essential to develop better applications. Also, the ef
29#
發(fā)表于 2025-3-26 13:01:26 | 只看該作者
1610-3947 ti-database mining applications.Multi-database mining has been recognized recently as an important and strategically essential area of research in data mining. In this book, we discuss various issues regarding the systematic and efficient development of multi-database mining applications. It explain
30#
發(fā)表于 2025-3-26 18:32:15 | 只看該作者
https://doi.org/10.1007/978-3-662-66456-8i-database mining techniques. Experimental results are provided and they are reported for both real-world and synthetic databases. They help us assess the effectiveness of the pipelined feedback model.
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