One-dimensional histogram ordering

In the ordered list, users can see the numerical detail about the distribution of each dimension in an orderly manner. The numerical detail includes the five-number summary of each dimension and the mean and the standard deviation. The numerical score values are also shown at the third column whose background is color coded using the same color mapping as in the score overview. 

Since different users may be interested in different features in the datasets, it is desirable to allow users to customize the available set of ranking criteria. However, we have 

2 Uniformity of Distribution (0 to Number of Bins) For the uniformity test, an information-based measure called entropy was used. Given k bins in a histogram, the entropy of a histogram, H, is defined as 

High entropy means that values of the dimension are from a uniform distribution and the histogram for the dimension tends to be flat. While knowing a distribution is uniform is helpful to understand the dataset, it is sometimes more informative to know how far a distribution deviates from uniform distribution since a biased distribution sometimes reveals interesting outliers. 

3 Number of Potential Outliers (0 to n) To count outliers in a distribution, we used the 1.5 x IQR [interquartile range the difference between the first quartile (Qi) and the third quartile (03)] criterion that is the basis of a rule of thumb in statistics for identifying suspected outliers (Moore and McCabe, 1999). An item of value d is considered as a suspected (mild) outlier if 

---

Before going into the details on how to set the dimensionality, we first recommend taking a look at the sample sets to verify that no clear outlying samples will immediately influence (negatively) the models. One can start by examining all the spectra overlaid in order to see rapidly whether some are different. A histogram of the concentration values may help to assess their... 

---