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Evaluation Tools for SOMs


When the results of the learning process are to be analyzed, the tools described here can be used to evaluate the qualitative properties of the SOM. In order to provide this functionality, a special panel was added. It can be called from the manager panel by clicking the button and is displayed in figure gif. Yet, the panel can only be used in combination with the control panel.

Figure: The additional KOHONEN (control) panel

  1. Euclidian distance
    The distance between an input vector and the weight vectors can be visualized using a distance map. This function allows using the SOM as a classifier for arbitrary input patterns: Choose Act_Euclid as activation function for the hidden units, then use the button in the control panel to see the distance maps of consecutive patterns. As green squares (big filled squares on B/W terminals) indicate high activations, green squares here mean big distances, while blue squares represent small distances. Note: The input vector is not normalized before calculating the distance to the competitive units. This doesn't affect the qualitative appearance of the distance maps, but offers the advantage of evaluating SOMs that were generated by different SOM-algorithms (learning without normalization). If the dot product as similarity measure is to be used select Act_Identity as activation function for the hidden units.

  2. Component maps
    To determine the quality of the clustering for each component of the input vector use this function of the SOM analyzing tool. Due to the topology-preserving nature of the SOM algorithm, the component maps can be compared after printing, thereby detecting correlations between some components: Choose the activation function Act_Component for the hidden units. Just like displaying a pattern, component maps can be displayed using the LAYER buttons in the KOHONEN panel. Again, green squares represent large, positive weights.

  3. Winning Units
    The set of units that came out as winners in the learning process can also be displayed in SNNS. This shows the distribution of patterns on the SOM. To proceed, turn on units top in the setup window of the display and select the winner item to be shown. New winning units will be displayed without deleting the existing, which enables tracing the temporal development of clusters while learning is in progress. The display of the winning units is refreshed by pressing the button again.

    Note: Since the winner algorithm is part of the KOHONEN learning function, the learning parameters must be set as if learning is to be performed.

next up previous contents index
Next: Autoassociative Networks Up: SOM Implementation in Previous: Building and Training
Tue Nov 28 10:30:44 MET 1995