Data Preprocessing in Data Mining

本文介绍了数据挖掘前的数据预处理技术,包括数据清洗、数据集成、数据缩减及数据转换等步骤,旨在提高挖掘模式的质量和效率。

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reference: Chapter 3 in book <<Data Mining Concepts and Techniques>>, 3rd Ed by Han, etc.

Data preprocessing techniques aims to improve the quality of pattern mined and/or the time required for mining:

  • Data cleaning, which removes noise and correct inconsistencies
  • Data integration, which merges multiple data sources into a data store, i.e. data warehouse
  • Data reduction, which reduces data size by i.e. aggregating, redundancy reduction, clustering, etc
  • Data transformation, which scales data into a smaller range (i.e. normalization to 0 to 1)

The above techniques can work together. Data characteristics, attribute features are useful for these techs.

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