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Futher development

ENTIRE is a prototype tool and it lacks certain important features. One such feature is visualization using 3D-graphics. While 2D-images are adequate in most cases, they sometimes pale when compared with the visualization power of 3D-presentation. A special benefit would be if the 3D-presentation could be made interactive. The Virtual Reality Modeling Language (VRML) offers intriguing possibilities for this by making e.g. data space fly-throughs easy to implement.

Currently the tools to handle hierarchical maps are very primitive. A hierarchial structuring of SOMs should be made much easier to implement and explore. For example when analysing a top-level map, the user should be able to easily request the values of bottom-level variables corresponding to a certain map unit.

ENTIRE supports only the rectangular map topology. While it would be impossible and even unnecessary to cover all possible structure topologies, there are a few very important ones that should be offered to the user, i.e. the toroidal topology and the cylinder.

The postprocessing is possibly the most important phase of analysis when using the SOM. ENTIRE offers some basic tools for the labeling postprocessing method. When analysing a data set one cannot avoid noticing that the basic tools are in many cases very limited. A proper data mining environment should offer the user a much more flexible way of handling the labels: a spreadsheet or a scripting language. Another important postprocessing method currently totally missing from ENTIRE are the different kinds of methods for automatic clustering discussed in section 3.3.

Figure 4.1: The vector display window of ENTIRE. On top left, inside the frames the name of the data set. Below it the vectors belonging to that data set, with the fourth vector selected. On bottom left, the labels of the selected data vector. On right, the selected vector itself is shown. The components are divided into groups, and for each component the component name, original (unscaled) value and relative value are shown. The relative value is obtained by comparing the component value to the minimum and maximum values of that component in the data set.

Figure 4.2: The u-matrix and the component plane visualizations of ENTIRE. The top left image is the u-matrix and the rest of the images are the component planes of the SOM. Each image is shown as a gray scale image with the title on top and the legend of colours with corresponding values on the right. The actual values of the u-matrix are the borders between units. The units themselves (the hexagons) are coloured according to the median of the surrounding edges. Big values of the u-matrix correspond to a great distance between weight vectors of the map units, while small values mean that the map units are close to each other in the input space. Since big values are represented by dark colours, big gaps in the input space can be seen as dark borders between map units, while uniform areas can be seen as light areas. In the component plane images each hexagon represents one map unit, and its colour tells the value of the component in that unit. Hexagons in same place on different images correspond to the same map unit and show the values of the components in the weight vector of that unit.

Figure 4.3: The colour controls (a) of ENTIRE and an example of a component plane with four different kinds of colour maps.

Figure 4.4: The visualization of the Sammon's mapping. The map projections of map units are represented by the black dots. The lines between dots show the neighborhood relations between map units.

Figure 4.5: Data histograms of a data set. The histogram of data vectors can be shown either as absolute numbers (a) or as squares the sidelength of which is proportional to the number of vectors in classified to a certain map unit (b).

Figure 4.6: The BMU search tool of ENTIRE (a) and the results of the search plotted on a component plane (b). The first BMU of the search vector is marked with the biggest rectangle. The second-BMU is marked with the second biggest rectangle, and so on. The sidelengths of the squares are proportional to the quantization errors as related to the quantization error of the first BMU.

Figure 4.7: Trajectory controls of ENTIRE (a) and the visualization of a trajectory on a component plane (b). The trajectory of the time-series data set has been formed from the BMUs of 5 consecutive data vectors with arrows indicating the direction of movement over time. For the current data vector, four BMUs are displayed.

Figure 4.8: Labels of a SOM shown on top of a component plane. The different labels have been added to the map using different kinds of labeling procedures. The ``high'' labels have been added to the map using component value range labeling. The ``selected'' labels have been added by manually selecting map units and giving them a common label. The ``type'' labels have been added using the autolabeling procedure: the labels of three sample vectors have been given to their corresponding BMUs. Finally using the BMU searching tool four BMUs of the ``type3'' sample vector were searched and they were given a common label ``bmu''.

next up previous contents
Next: Pulp and Paper Industry Up: A Data Mining Tool Previous: Postprocessing

Juha Vesanto
Tue May 27 12:40:37 EET DST 1997