When looking at a line chart in Spotfire you can use K-Means or Line Similarity tools to find clusters or patterns or matching trends respectively. In 3.1, Euclidian Distance has been added as an optional similarity measure to use when performing K-Means Clustering or Line Similarity calculations. Previously, K-Means and Line Similarity only supported correlation for finding patterns which meant that similarity of trends was judged based on the relative shape of the lines no matter how far apart they might have been by the Y-axis. Now that Euclidian Distance is offered, you can cluster or seek similar trends based on their nearness to one another in Y-axis space.  See the illustrations below based on the Line Similarity tool:

We start with thousands of trends, but one particular trend is of interest and we want to find out which other trend line are most similar to the target trend.

Go to Tools > Options and run the Line Similarity tool.  If you choose “Correlation” as a similarity metric, then similarity is judged based on the shape of the line.  Here is what the plot looks like after filtering down to the top 10 results using this method.

But if you choose the new Euclidian Distance similarity option in 3.1 the top 10 most similar results to the target trend will be based on nearness rather than shape. You can see that the trends are not necessarily shaped exactly similar to the target trend, but they are located closely to the target trend.