Data Preprocessing: Overview and Major Tasks

What is the primary purpose of data integration according to the text?

Which of the following is NOT mentioned as a major task in data preprocessing according to the text?

What technique is mentioned in the text for detecting data value conflicts?

What is the purpose of data scrubbing?

Which of the following is mentioned in the text as a tool for data migration and integration?

What is the relationship between data cleaning and data auditing according to the text?

What is one of the major reasons for data being considered dirty in the real world?

Which of the following is an example of inconsistent data according to the text?

Why might missing data need to be inferred according to the text?

What is a common cause of missing data according to the text?

Which process involves reducing the dimensionality or numerosity of the data, according to the text?

What is a characteristic of noisy data as described in the text?

What is the main purpose of data cleaning in data preprocessing?

Which of the following is NOT a measure of data quality?

What is the purpose of data integration in data preprocessing?

Which of the following is NOT a task in data preprocessing?

What is the purpose of data reduction in data preprocessing?

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

Data quality is measured by accuracy, completeness, consistency, timeliness, believability, and interpretability.
Inaccurate data can be incorrect or wrong.
Incomplete data can be missing or unavailable.
Inconsistent data can be modified or dangling.
Untimely data can be outdated.
Unbelievable data can be untrustworthy.
Uninterpretable data can be difficult to understand.

Data cleaning involves detecting and correcting errors, inconsistencies, and inaccuracies.
Data cleaning methods include combined computer and human inspection, data discrepancy detection, data scrubbing, and data auditing.
Data scrubbing uses simple domain knowledge to detect errors and make corrections.
Data auditing analyzes data to discover rules and relationships to detect violators.

Data integration combines data from multiple sources into a coherent store.
Data integration involves entity identification, detecting and resolving data value conflicts.
Entity identification involves identifying real-world entities from multiple data sources.
Data value conflicts occur when attribute values from different sources are different.

Data reduction involves reducing the size of the data while preserving its integrity.
Data reduction techniques include dimensionality reduction, numerosity reduction, and data compression.

Data transformation involves converting data into a suitable format for analysis.
Data discretization involves converting continuous data into discrete data.
Normalization and concept hierarchy generation are techniques used in data transformation and discretization.