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Executives too frequently assume there's no time (let alone budget) for knowledge discovery.
Today's big data is forcing researchers to find new techniques for knowledge discovery and data mining.
Mining sequential patterns (MSP) is an important task for knowledge discovery and data mining (KDD).
This paper describes a data mining environment for knowledge discovery in bioinformatics applications.
This paper tackles problems encountered in mining of incomplete data for knowledge discovery of construction databases.
Examples are given of the use of large research databases for knowledge discovery.
This paper presents a generic framework for knowledge discovery in massive BAS data using DM techniques.
We propose a snail shell process model for knowledge discovery via data analytics (KDDA) to address these challenges.
Furthermore, automation of such custom data analysis workflow is necessary for biologists to apply this powerful platform for knowledge discovery.
The grid can play a significant role in providing an effective computational support for knowledge discovery applications.
The system is intended for knowledge discovery from various data sources, including structured quantitative data and text collections.
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