17. Typhoon/Hurricane Tracking Objective: Intensity, track (land falling, recurvature) Object: The space-time track of unusually low sea- surface air pressure in the x-y-z plane Data: potential temperature, horizontal velocity, vertical velocity, relative humidity, horizontal wind, etc Data: Hundreds and thousands of gigabytes within a specific time interval
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21. Data Mining in Hyperspectral Images 1. Objective Classification, Pattern Recognition 2. Object Spectral Signatures of Objects 3. Data Spectral, Non-spectral Data 4. Data Volume e.g. : AVIRIS : from 0.4 to 2.45 micrometers, 224 bands HYDICE : from 0.4 to 2.5 micrometers, 210 bands Hyperion : from 0.4 to 2.5 micrometers, 220 bands, 30 meter resolution
22. The Objective of Knowledge Discovery and Data Mining Fayyad: The discovery of non-trivial, novel, potentially useful and interpretable knowledge/information from data Data Information Knowledge Decision
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24. Main Tasks of Spatial Knowledge Discovery and Data Mining 1. Clustering 3. Association 2. Classification Spatial Relations Temporal Relations Spatial-temporal Relations * In particular : the local-global issue 4. Processes
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27. If the training samples are treated as an imaginary image with expression: Then the corresponding blurred image f (x, σ , D l ) at scale σ can be specified by
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44. a) b) Ms-time plot of clustering results for earthquakes (Ms≥4.7): a) 2 clusters in the 74th~112th scale range; b) 18 clusters at the 10th scale step
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49. 5. Scale Space Clustering Scale-Space Filtering for Simulated Data
50. 5. Scale Space Clustering Scale-Space Filtering for Remote-Sensing Data Clustering Tree Quasi-Light
79. Trees by Classification and Regression Tree (CART) MSW 6/12/18: Maximum Sustained Wind of TC 6/12/18 hours before recurvature. 0: Recurve,1: Straight
103. Log-log plots of F q (s) versus s for the daily rainfall time series of station 56691 in Pearl River basin (left) and Station Chuantang in East River basin (right) with q =2.
104. The h ( q ) curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right).
105. The curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right).
106. The curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right)
107. The curves of daily rainfall time series of 5 stations in the Pearl River basin
108. The curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right).
109. The curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right). The real lines are their cascade model fitting.
110. The correlation relationship between the altitude of the rainfall stations in the East River basin and the D (2) value of the rainfall time series.
111. Elevation of rainfall stations in the East River basin with the D2 values of their rainfall data. Elevation (m above MSL)