Discretization algorithms serve as a critical pre-processing step within data mining and machine learning, transforming continuous attributes into discrete categories to enhance the ...

The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from ...

This would risk missing important categories. With data mining it is possible to let the data itself determine the groups. This is one of the black-box type of algorithms that are hard to understand.

Here, appropriate data mining algorithms are selected based on the goal of the mining — e.g., classification, regression, clustering, etc. Different algorithms are better suited for different ...

Discover how data mining uncovers patterns in big data to boost marketing, sales, fraud detection, and more using advanced techniques.

Data mining techniques have been widely used for extracting knowledge from large amounts of data. Monitoring deforestation is utmost important for the developing countries.

So if race is disproportionately (but not explicitly) represented in the data fed to a data-mining algorithm, the algorithm can infer race and use race indirectly to make an ultimate decision.

This article discusses statistical and data mining methods used in credit scoring, highlighting their applications and effectiveness in evaluating credit risk.