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Data Analysis Machine Learning and Applications Episode 2 Part 8

Tham khảo tài liệu 'data analysis machine learning and applications episode 2 part 8', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 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 Shrinking of Inhabitants and Employment Shrinking but influx Not Classified LZ Growing of Employment Growing of Inhabitants Growing of Inhabitants and Employment 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 processes. 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 system 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. 318 Martin Behnisch and Alfred Ultsch References BEHNISCH M. 2007 Urban Data Mining. Doctoral thesis Karlsruhe TH . DEMSAR U. 2006 Data Mining of geospatial data .