Which technique uses different entries from within the same data set to represent the data?

The technique that uses different entries from within the same data set to represent the data is shuffling. Shuffling involves rearranging the order of entries within a dataset while maintaining the overall dataset's original characteristics and relationships. This process can help in various scenarios, such as randomizing data for training machine learning models or ensuring that data sampling does not exhibit bias.

Shuffling is particularly useful to ensure that any patterns within the data do not affect the evaluation of models or outcomes. By mixing up the entries, researchers and analysts can create a randomized representation of the dataset, making it easier to draw general conclusions without the influence of ordered or patterned data.

In contrast, the other techniques focus on different aspects of data manipulation. For example, data normalization is aimed at adjusting the scale of data points to mitigate disparities and ensure consistency across datasets. Data segmentation involves dividing a dataset into smaller, distinct groups for more detailed analysis. Data aggregation refers to the process of combining multiple data points into a summary or total, which can provide insights at a higher level but does not involve rearranging entries.