  Data mining is about obtaining new knowledge from existing datasets. However,
the data in the existing datasets can be scattered, noisy, and even incomplete.
Although lots of effort is spent on developing or fine-tuning data mining
models to make them more robust to the noise of the input data, their qualities
still strongly depend on the quality of it. The article starts with an overview
of the data mining pipeline, where the procedures in a data mining task are
briefly introduced. Then an overview of the data preprocessing techniques which
are categorized as the data cleaning, data transformation and data
preprocessing is given. Detailed preprocessing methods, as well as their
influenced on the data mining models, are covered in this article.
