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This integration also allows efficient preparation of product data for various data analysis and data mining processes.
The usage of scientific workflows allows composing convenient tools for modelling data mining processes and for simulation of real-world time- and resource-consuming data mining problems.
Data mining processes data from different perspectives into useful knowledge, and becomes an important component in designing intelligent decision support systems (IDSS).
The off-line prognostics system uses fleet wide system data and performs deep data mining processes that could not be performed onboard in real time due to the lack of resources and time criticality.
Extracting frequent tree patterns which are hidden in data trees is central for analyzing data and is a base step for other data mining processes including association rule mining, clustering and classification.
In EDEN, the scented axis widgets are augmented with information from automated data mining processes (e.g., statistical filters, automatic axis arrangements, regression mining, correlation mining, and subset selection capabilities) that highlight potentially relevant associations and reduce knowledge discovery timelines.
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Adding to the above components are privacy and security mechanisms, open standards and standard rules, as well as conceptual frameworks (data mining process, context recognition process, context information processing, etc).
Data mining process includes application of three preprocessing methods: noise filtering, stepwise regression and data clustering.
Firstly, a data mining process has carried out using Rapid Miner® software [14].
Fig. 2 The data mining process applied to urban sustainability problems.
A study of aging in a healthy population was selected to represent this data mining process.
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