Data Mining: Concepts and Techniques¶
Data Mining: Concepts and Techniques.
Cited by¶
1 citation across 1 artifact.
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Domain-specific¶
- Evolutionary data mining
- … initialization, fitness and objectives, selection, crossover and mutation, population replacement, constraints, stopping, randomness, leakage control, and external validation are explicit. to An applied instance preserves the same invariant under a changed scale, notation, jurisdiction, dataset, or implementation..
This sourceThe process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm. ==Process== ===Data preparation=== Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned, which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results. Jiawei Han, Micheline Kamber Data Mining: Concepts and Techniques (2006), Morgan Kaufmann.
- … initialization, fitness and objectives, selection, crossover and mutation, population replacement, constraints, stopping, randomness, leakage control, and external validation are explicit. to An applied instance preserves the same invariant under a changed scale, notation, jurisdiction, dataset, or implementation..
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