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Titlebook: Data Analysis and Pattern Recognition in Multiple Databases; Animesh Adhikari,Jhimli Adhikari,Witold Pedrycz Book 2014 Springer Internatio

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樓主: cherub
21#
發(fā)表于 2025-3-25 07:13:43 | 只看該作者
22#
發(fā)表于 2025-3-25 08:42:50 | 只看該作者
P. Leif Bergsagel,W. Michael Kuehlresting as well as challenging when we are required to identify patterns and associations in multiple large data sources. While dealing with the domain of multiple large data sources, it has been observed that many patterns are specific to this domain; also some patterns are extensions of classical patterns.
23#
發(fā)表于 2025-3-25 15:43:31 | 只看該作者
https://doi.org/10.1007/978-3-319-03410-2Data Analysis; Intelligent Systems; Multiple Databases; Pattern Recognition
24#
發(fā)表于 2025-3-25 19:20:14 | 只看該作者
978-3-319-37727-8Springer International Publishing Switzerland 2014
25#
發(fā)表于 2025-3-25 23:44:24 | 只看該作者
26#
發(fā)表于 2025-3-26 00:16:08 | 只看該作者
27#
發(fā)表于 2025-3-26 07:30:24 | 只看該作者
Synthesizing Global Patterns in Multiple Large Data Sources, low quality 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 databases.
28#
發(fā)表于 2025-3-26 12:29:03 | 只看該作者
Synthesizing Global Exceptional Patterns in Different Data Sources,the number of branches of a multi-branch company is increasing over time. Thus, it is important and timely to study data mining carried out on multiple data sources. A global exceptional pattern describes interesting individuality and specificity of few branches. Therefore, it is interesting to identify such patterns.
29#
發(fā)表于 2025-3-26 12:59:48 | 只看該作者
30#
發(fā)表于 2025-3-26 18:23:49 | 只看該作者
Histone Acetylation And MethylationOrganizations that collect data from their multiple branches are common. Also, many established organizations possess data for a long period of time. Due to a spectrum of analyses, such data often need to be sub-divided into smaller databases.
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