582364 Data mining, 4 cu Lecture 9:

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Concept hierachies in spatial data � Non-Spatial Attributes � e.g. “Exactum” generalizes to “Office Building” generalizes to “Building” � Spatial-to-Nonspatial Attributes � e.g. +60° 12' 13.19” N, +24° 57' 44.32” generalizes to “Kumpula Campus, Helsinki” (as a concept) � Spatial-to-Spatial Attributes � e.g. Kumpula (as a spatial region) generalizes to Helsinki (as a spatial region) Data mining, Spring 2010 (Slides adapted from Han & Kamber + Antti Leino)
Spatial Data mining tasks: Classification � Aim: classify locations on map into predefined classes � Example: � monitoring type of crops grown in the fields � Data from infrared/normal images taken by a satellite � used (at least) in Europe and USA to detect subsidy fraud � Closely related task: image segmentation Data mining, Spring 2010 (Slides adapted from Han & Kamber + Antti Leino)
Page 1 and 2: 582364 Data mining, 4 cu Lecture 9:
Page 3 and 4: Spatial Autocorrelation � First l
Page 5 and 6: Kinds of Spatial data: graph data
Page 7: Spatial relations � Spatial objec
Page 11 and 12: Spatial Data mining tasks: Clusteri
Page 13 and 14: Special case: co-location pattern m
Page 15 and 16: Grid-based co-location mining 1. Di
Page 17 and 18: Case study in Spatial Data mining
Page 19 and 20: Socio-economic indicators 1. popula
Page 21 and 22: Super-patterns � The authors stat
Page 23 and 24: Geographical distribution of the su
Page 25 and 26: Course exam � Tuesday 4.5. at 9-1

582364 Data mining, 4 cu Lecture 9:

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