A large variety of issues influence the success of data mining on a given problem. Two primary and important issues are the representation and the quality of the dataset. Specifically, if much redundant and unrelated or noisy and unreliable information is presented, then knowledge discovery becomes a very difficult problem. It is well-known that data preparation steps require significant processing time in machine learning tasks. It would be very helpful and quite useful if there were various preprocessing algorithms with the same reliable and effective performance across all datasets, but this is impossible. To this end, we present the most well-known and widely used up-to-date algorithms for each step of data preprocessing in the framework of predictive data mining.

中文翻译：

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预测数据挖掘中的数据预处理

各种各样的问题会影响数据挖掘在给定问题上的成功。两个主要和重要的问题是数据集的表示和质量。具体来说，如果提供了很多冗余和不相关或嘈杂和不可靠的信息，那么知识发现就成为一个非常困难的问题。众所周知，数据准备步骤在机器学习任务中需要大量的处理时间。如果有各种预处理算法在所有数据集上具有相同的可靠和有效性能，那将是非常有帮助和非常有用的，但这是不可能的。为此，我们在预测数据挖掘框架中为数据预处理的每个步骤展示了最知名和广泛使用的最新算法。