There are three quartile values—a lower quartile, median, and upper quartile—which divide the data set into four ranges, each containing 25% of the data points: ...

Derive your third quartile based on whether your data set includes an even or odd set of values. With an odd number of values, multiply N by 3, add 1 and divide the result by 4.

Then, a data-driven outlier elimination approach combining quartile method and density-based clustering method is proposed. First, the quartile method is used twice for eliminating sparse outliers.

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