Which of the following data-classification methods selects class break levels by looking for gaps between data values?

The Natural Breaks classification method is particularly effective for selecting class break levels based on the distribution of data values. It uses statistical techniques to identify inherent groupings within the data, focusing specifically on the gaps or "natural breaks" that occur in the dataset. By doing so, it minimizes intra-class variance and maximizes inter-class variance, ensuring that each class contains similar values while clearly distinguishing between different classes.

In practice, this method often results in class intervals that better represent the underlying patterns within the data. As a result, Natural Breaks is favored for datasets where the distribution is irregular or non-uniform, as it adapts to the structure of the data rather than forcing it into pre-defined categories. This approach can provide more meaningful visual and analytical insights compared to other methodologies that rely on fixed intervals or arbitrary partitions.