Clustering non-numeric -- or categorial -- data is surprisingly difficult, but it's explained here by resident data scientist Dr. James McCaffrey of Microsoft Research, who provides all the code you ...

Quantitative Financial Market Analysts work in finance using numerical or quantitative techniques. Remote Sensing Scientists use sensors to analyze data and solve regional, national, and global ...

If the results of the numerical calculation agree with actual data, then it works. It's that simple. Sometimes we can use numerical methods to find the trajectory of an object with forces acting ...

This is when a value is found within the data set, using the line of best fit. The value was not originally plotted, but can be read off the line of best fit. This is when a value is found outside ...

Typically what people do is find a “fit” for those values using numerical data. For example, you might have an experiment where someone measured the quantity of active RAF at different drug doses.

Meteorologists use these equations to create what's known as numerical weather models to predict what's going to happen in the atmosphere.

In this article I'll show you how to cluster non-numeric, or mixed numeric and non-numeric data, using a clever idea called category utility (CU). [Click on image for larger view.] Figure 1.