Parallel Coordinates In Machine Learning

The parallel coordinate technique makes use of the concept of networking a multi-dimensional point to some axes and all of these are parallel to each other. In these techniques, single data elements are being plotted across many dimensions and these dimensions are connected to the y-axis and each object of the data is shown along the axis as a series of multidimensional data and a lot of these dimensions are being organized and expanded by this technique.

When there is a line that forms a single polygonal line for all the occurrences represented, then it connects the individual coordinate mappings. Therefore the number of dimensions that id being represented is not limited at all. This visualization technique is applicable in areas such as computer vision, air traffic control, computational geometry, robotics, and data mining.

A good advantage of this visualization technique is that it usually represents lots of dimensions without limits. Though, you can encounter a case such as the polygonal lines being overlapped which cause difficulty in identifying characteristics in the data and this caused when you have many points that are being represented when engaging the parallel coordinate approach.