Data Reduction and Summarization

Deduplication is a data reduction technique that involves identifying and removing duplicate records from a dataset, resulting in a smaller and more concise dataset.

Mean is a data summarization technique that involves calculating the average value of a numerical attribute across a group of records, providing a measure of central tendency.

Mode is a data summarization technique that involves identifying the most frequently occurring value of a categorical attribute across a group of records, providing a measure of central tendency.

Sampling is a data reduction technique that involves selecting a subset of records from a dataset that is representative of the entire dataset, allowing for analysis of a smaller and more manageable dataset.

Range is a data summarization technique that involves calculating the difference between the maximum and minimum values of a numerical attribute across a group of records, providing a measure of variability.

Aggregation is a data reduction technique that involves grouping records based on common attributes and aggregating their values, resulting in a smaller and more concise dataset.

Dimensionality Reduction is a data reduction technique that involves removing irrelevant or redundant attributes from a dataset, resulting in a smaller and more manageable dataset.

Sum is a data summarization technique that involves calculating the sum of all values of a numerical attribute across a group of records, providing a measure of total value.

Factor Analysis is a data reduction technique that involves replacing multiple attributes with a single attribute that captures their combined information, resulting in a smaller and more manageable dataset.

Median is a data summarization technique that involves identifying the middle value of a sorted list of values of a numerical attribute, providing a measure of central tendency.

Outlier Detection is a data reduction technique that involves removing outliers from a dataset, resulting in a smaller and more representative dataset.

Percentage is a data summarization technique that involves calculating the proportion of records that satisfy a certain condition, providing a measure of relative frequency.

Principal Component Analysis is a data reduction technique that involves replacing a set of attributes with a smaller set of attributes that captures their essential information, resulting in a smaller and more manageable dataset.

Count is a data summarization technique that involves calculating the number of records in a dataset, providing a measure of the size of the dataset.

Correlation Analysis is a data reduction technique that involves removing highly correlated attributes from a dataset, resulting in a smaller and more manageable dataset.