Data Preprocessing Quiz

Data preprocessing involves data cleaning, data integration, data reduction, and data transformation.

Data quality measures include accuracy, completeness, consistency, timeliness, believability, and interpretability.

One of the major tasks in data preprocessing is data reduction, which includes dimensionality reduction and data compression.

Incomplete data refers to lacking attribute values, lacking certain attributes of interest, or containing only aggregate data.

Noisy data refers to data containing noise, errors, or outliers.

Data preprocessing involves only four major tasks: data cleaning, data integration, data reduction, and data transformation.

Data transformation and data discretization are the same process in data preprocessing.

In data quality measures, timeliness refers to how easily the data can be understood.

Noisy data can include errors, outliers, or missing attribute values.

Data integration in data preprocessing involves the integration of multiple databases, data cubes, or files.