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Numerous tools suitable for data mining in biology are available, yet the selection of an appropriate tool is non-trivial.
So the important question is, how can we measure the effectiveness of classification results in data mining?
His research interests include machine learning, genetic algorithms, data mining, and network security.
In the following, we illustrate the potential of sparse grids for problems of a higher dimensionality in the context of numerical quadrature and data mining.
However, the rules generated by the data mining methods are rarely used to provide greater insight into the original problem domain.
Subjective techniques generally operate by comparing the beliefs of a user against the patterns discovered by the data mining algorithm.
These goals can be achieved by using various general data mining methods, which are described below.
In data mining and machine learning communities, several classification algorithms have been proposed, where most of these produce classifiers with an acceptable error rate.
The range of applications spans from numerical programming, symbolic computing, computer vision, to telephony and data mining.
As her research progressed, she used more complex case representations, reasoning and learning strategies, and data mining techniques for pattern recognition.
The majority of the data mining literature concentrates on the discovery of accurate and comprehensible patterns.
We are exploring the use of fuzzy data mining and concepts introduced by the semantic web to operate in synergy to perform distributed intrusion detection.
On the positive side, data mining and machine learning can be used to generalise news reports, and thereby generate knowledge for coherence systems.
While the term ' data mining' is relatively recent, techniques for identifying patterns in data are much older.
This review provides a discussion of and pointers to efficient algorithms for the common data mining tasks in a mathematical framework.
