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Data mining methods have been required in both commercial and non-commercial areas.
A number of data mining methods have been developed to deal with one or more of these challenges.
Although many sequential frequent itemset mining methods have been proposed, there is a clear limit to the performance that can be achieved using a single thread.
Although several data stream mining methods have been proposed, an evaluation of such methods in the context of traffic applications is yet missing.
For many years it has been studied in all kinds of application area and thus many data mining methods have been developed and applied to practice.
While most of the mining methods have long gone out of fashion, they are still useful to know, said several professionals in the mining industry, who come to the competition to act as judges — and scout for talent.
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Popularity of in silico mining methods has led to the construction of various microsatellite databases in recent years, each with a different emphasis.
Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery.
Several text-mining methods have been developed to automatically extract localization information from the biomedical abstracts (16 18).
Given the challenges for statistical approaches, non-statistical methods such as machine-learning and data-mining methods have been proposed for the study of genetic interactions [ 15, 16].
Text-mining methods have shown promising results in the biomedical domain, where a considerable amount of methods and applications have been published [2, 3].
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