Year: 2019
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Li, Canchen
Institution: Technical University of Munich
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2952736244