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Titlebook: Methodologies for Knowledge Discovery and Data Mining; Third Pacific-Asia C Ning Zhong,Lizhu Zhou Conference proceedings 1999 Springer-Verl

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發(fā)表于 2025-3-30 10:04:31 | 只看該作者
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發(fā)表于 2025-3-30 13:36:09 | 只看該作者
Parallel SQL Based Association Rule Mining on Large Scale PC Cluster: Performance Comparison with Diecomes widely recognized. Currently database systems are dominated by relational database and the ability to perform data mining using standard SQL queries will definitely ease implementation of data mining. However the performance of SQL based data mining is known to fall behind specialized impleme
53#
發(fā)表于 2025-3-30 19:11:25 | 只看該作者
H-Rule Mining in Heterogeneous Databaseson rule to define a new heterogeneous association rule (h-rule) which denotes data association between various types of data in different subsystems of a heterogeneous and multimedia database, such as music pieces vs. photo pictures, etc. Boolean association rule and quantitative association rule ar
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發(fā)表于 2025-3-31 00:24:32 | 只看該作者
An Improved Definition of Multidimensional Inter-transaction Association Ruleearch topic. Traditional association rule is limited to intra-transaction mining. Recently the concept of multidimensional inter-transaction association rule (MDITAR) is proposed by H.J. Lu. Based on analysis of inadequencies of Lu’s definition, this paper introduces a modified and extended definiti
55#
發(fā)表于 2025-3-31 01:33:44 | 只看該作者
Induction as Pre-processinge set of relevant attributes. The real world database from which knowledge is to be extracted usually contains a combination of relevant, noisy and irrelevant attributes. Therefore, pre-processing the database to select relevant attributes becomes a very important task in knowledge discovery and dat
56#
發(fā)表于 2025-3-31 07:43:39 | 只看該作者
Stochastic Attribute Selection Committees with Multiple Boosting: Learning More Accurate and More Ststrated great success in increasing the prediction accuracy of decision trees. Boosting and Bagging create different classifiers by modifying the distribution of the training set. Sasc adopts a different method. It generates committees by stochastic manipulation of the set of attributes considered a
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發(fā)表于 2025-3-31 10:46:15 | 只看該作者
58#
發(fā)表于 2025-3-31 15:38:24 | 只看該作者
applied to a wide range of contexts from various engineeringThis book thoroughly investigates the underlying theoretical basis of membrane computing models, and reveals their latest applications. In addition, to date there have been no illustrative case studies or complex real-life applications that
59#
發(fā)表于 2025-3-31 18:42:18 | 只看該作者
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發(fā)表于 2025-3-31 22:01:48 | 只看該作者
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