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Based on the objective function, the diversified (mathsf {GPAR}) mining problem is stated as follows.
ARs mining problem is formulated as a four-objective optimization problem.
The most commonly used distance measure for the data mining problem is the Euclidean distance, which is defined as begin{aligned} D(p_i, p_j) = left( sum _{l=1}^{d}|p_{il}, p_{jl}|^2 right) ^{1/2}, end{aligned} (3)where (p_i) and (p_j) are the positions of two different data.
The emerging substrings mining problem is defined as follows.
Thus the corresponding data mining problem is to detect a small number of cohesive, possibly overlapping clusters in the data while ignoring irrelevant data portions.
Moreover, when a graph-based pattern mining problem is transformed into a continuous optimization problem, it becomes easy to incorporate constraints representing prior knowledge.
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After the data mining problem was presented, some of the domain specific algorithms are also developed.
In the remaining of this section, we detail how the classic sequence mining problem was adapted to our context.
The scheduling problem and data mining problem are two typical real-world applications that could be solved by the PSO algorithms.
Prevention is the best cure As serious as the mine problem is in Colombia, there is no humanitarian de-mining in the country.
Although most definitions of data mining problems are simple, the computation costs are quite high.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com