582364 Data mining, 4 cu Lecture 9:

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Data � Objects are counties in USA � Characterized by their � geographical location (county ID #) � attributes f i (socio-economic indicators), and � label (c) indicating whether the county was won (c=1) or lost (c=0) by Barack Obama. o = id, f 1,..., f m,c [ ] Data mining, Spring 2010 (Slides adapted from Han & Kamber + Antti Leino)
Socio-economic indicators 1. population density, 2. % of urban population, 3. % of female population, 4. % of foreign-born population, 5. per capita income, 6. median household income, 7. % of population with high school or higher education, 8. % of population with bachelor degree or higher education, 9. % of population that is white, 10. % of population living in poverty, 11. % of houses occupied by owners, 12. percentage of population receiving social security benefits, 13. average social security monthly benefit 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 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: Case study in Spatial Data mining
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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