Data Analysis Machine Learning and Applications Episode 2 Part 8 docx

316 Martin Behnisch and Alfred Ultsch

Fig. 5. U*-Map (Island View)

Fig. 6. U*Matrix and Result of U*-C-Algorithm

5 Conclusion

The authors present a classification approach in connection with geospatial data. The

central issue of the grouping processes are the shrinking and growing phenomena in

Germany. First the authors examine the pool of data and show the importance for the

investigation of distributions according to the dichotomic properties. Afterwards it is

shown that the use of Emergent SOMs is an appropriate method for clustering and

Urban Data Mining Using Emergent SOM 317

Fig. 7. Localisation of Shrinking and Growing Municipalities in Germany

classification. The advantage is to visualize the structure of data and later on to define

a number of feasible cluster using U*C-algorithm or manual bestmatch grouping pro￾cesses. The application of existing visual methods especially U*-Matrix shows that it

is possible to detect meaningful classes among a large amount of geospatial objects.

For example typical hierarchical algorithm would fail to examine 12430 objects. As

such, the authors believe that the presented procedure of the wise classification and

the ESOM approach complements the former proposals for city classification. It is

expected that in the future the concept of data mining in connection with knowledge

discovery techniques will get an increasing importance for the urban research and

planning processes (Streich, 2005). Such approaches might lead to a benchmark sys￾tem for regional policy or other strategical institutions. To get more data for a deeper

empirical examination it is necessary to conduct field investigation in selected areas.