Select your ideal car.

(download LIONoso sample file)


This example shows how to choose your perfect car using LIONoso's data mining and visualization. You have several car models with different characteristics.

The problem is multi-objective – no single solution satisfies everybody. Individual preferences must be matched with existing possibilities and the proper choice is the result of personal compromises based on knowledge.

The use case is a toy example, you can easily substitute cars with your set of options for making a proper and informed decision.

LIONoso sample visualization(s): histogram

A histogram displays tabulated frequencies, shown as bars. It shows the proportion of cases that fall in each category. One immediately identifies how the different attributes (engine size, price, fuel consumption, ...) are distributed and gets a first idea about the space of possibilities. LIONoso visualization a data mining image

LIONoso sample visualization(s): bubble-chart and parallel coordinates

More details can be visualized with a colored bubble-chart (price, horse power, weight and engine size in this case). The attributes to be shown as X and Y coordinates, color and size of the bubble can be immediately changed with a couple of clicks. Below a second visualization (parallel coordinates) is synchronized with the bubble-chart. When a bubble is clicked, the values of all attributes are shown as a gray line in the parallel coordinates display. If filters are active in the parallel coordinates display, only the filtered cases appear in the bubble-chart, etc. Trying the effectiveness of seeing the same data with synchronized views is much faster than explaining. LIONoso visualization a data mining image

LIONoso sample visualization(s): navigation, clustering and parallel coordinates

Clustering is a way to group different cases together. In this case vehicles are grouped according to the given characteristics. After grouping, one prototype case for each group can be visualized. This is a very effective way to compress the information and concentrate on a relevant subset of possibilities, to then go back to the details once the favourite cluster (group) has been identified. A parallel coordinate display can be present to filter on each coordinate. LIONoso visualization a data mining image

Download the LIONoso-ready data file: cars.lion