582364 Data mining, 4 cu Lecture 9:

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Spatial Data Mining: Motivation � In many applications, the interesting patterns are location dependent � Classical example is the 1854 cholera outbreak in London � Deaths were spatially clustered in a polluted water pump in Soho, as shown by the map by John Snow � The connection of cholera and polluted water was not known at the time � Nowadays, data mining is used to monitor disease outbreaks � Local expert in Kumpula: Dr. Roman Yangarber Data mining, Spring 2010 (Slides adapted from Han & Kamber + Antti Leino)
Spatial Autocorrelation � First law of geography: “Everything is related to everything, but nearby things are more related than distant things.” � Spatial data tends to be highly auto-correlated: � The value of property in one location predicts the property in a nearby location � This property underlies most spatial data analysis tasks Data Mining: Principles and Algorithms 4/26/10 3
Page 1: 582364 Data mining, 4 cu Lecture 9:
Page 5 and 6: Kinds of Spatial data: graph data
Page 7 and 8: Spatial relations � Spatial objec
Page 9 and 10: Spatial Data mining tasks: Classifi
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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