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For this, the framework fuses two highly relevant approaches and embeds these into the building context: the generic model-based design methodology for cyber-physical systems and the cross-industry standard process for data mining.
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Knowledge Discovery Process: Data pre-processing for data mining, including data cleaning, selection, efficient sampling, and data reduction methods; evaluating, consolidating, and explaining discovered knowledge; data and knowledge visualization; interactive data exploration and discovery.
Data pre-processing is crucial for data mining.
In this paper, we present the rationales, design, and application of the needed advanced support for modeling and optimizing data flows for data mining and integration processes.
The increase in publically available microarray repositories provides a tremendous potential for data mining to unravel knowledge of cellular processes.
In the process of data mining for all PASS2 superfamilies using the MQ approach, additional validation was used for the homologues which failed to associate with the correct superfamily HMM.
They were developed primarily for data mining.
We developed our own software for data mining.
Additionally, ChEMBL provides tools and resources for data mining.
PJ was responsible for data mining using pathway analysis tools.
These datasets have provided great opportunities for data mining.
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