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Predictive Clustering of
Australian Vegetation Data
Saso Dzeroski, Valentin
Gjorgjioski, Matt White
(In alphabetical order)
The Relational Database
• A relational database was constructed
from the data provided by Matt White
• Three tables:
– sites - sites with corresponding attributes for
them
– species - species with corresponding
attributes for them
– sitemeasurements - table that links sites and
measurements and shows what is the cover
of given species to a given site
Attributes of sites
•
•
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•
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dem
twi
solar
thinvk
thk
eff_rain
mint_jul
max_feb
grnd_dpth
salinity
Attributes/properties of species
•
•
•
•
•
•
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Lifelook
Sprflow
Sumflow
Autflow
Winflow
Hitecat
Aquatic
Parasite
Fleshyf
Fleshyl
Data Preprocessing
• We remove all measurements with very low
cover (<0.5/+) and a few exotic species (as
suggested by Matt White (*)
• We changed the measured cover values from
nominal to numeric by using the following
mapping:
• 1 -> 2.5
• 2 -> 15
• 3 -> 37.5
• 4 -> 62.5
• 5 -> 87.5
Experimental setup
• The data mining task
– Descriptive attributes are description of sites
– Target attributes are aggregations of
characteristics of species
• The aggregation of species characteristics
is described below
Agg. of species characteristics
• We will do aggregation over characteristics of species by
using the following algorithm
• For each characteristic, we will create many target
attributes, one for each value of the given characteristic
• e.g. autflow has two values (0 and 1), so we will create
two target attributes:
– autflow=0
– autflow=1
• We calculate the value of each new attribute for a given
site Si, and given species Sp so that:
autflow1( Si, Sp) 
 cov er (Si, Sp)
SpSi , autflow( Sp ) 1
cov er ( Si )
cov er ( Si ) 
 cov er (Si, Sp)
SpSi
Experimental setup
• Data mining approach
– Use PCTs for multi-target prediction
– With descriptive and target attributes as
described above
– The SW package CLUS is used
An example tree
• Maxsize=16 (8lvs)
Description of clusters
•
Description of clusters given in details in excel table
– Site properties
• twi > 815
• max_feb > 3108
– Species properties (Cluster A):
• lifelook=SS
19%
• sprflow=1
76%
• sumflow=1
58%
• autflow=‘’
64%
• winflow=‘’
52%
• hitecat=1
36%
• aquatic=‘’
99%
• parasite=‘’
100%
• fleshyf=‘’
91%
• fleshyl=
80%
Description of clusters
•
Description of clusters given in details in excel table
– Site properties
•
•
•
•
twi > 815
max_feb <= 3108
numberspp > 9
thinvk > 4.758
– Species properties (Cluster B):
•
•
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•
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•
•
•
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lifelook=S
sprflow=1
sumflow=1
autflow=
winflow=
hitecat=2
aquatic=
parasite=
winflow=1
fleshyf=
fleshyl=
26%
80%
69%
72%
67%
23%
95%
99%
33%
90%
97%
Description of clusters
•
Description of clusters given in details in excel table
– Site properties
•
•
•
•
twi > 815
max_feb <= 3108
numberspp > 9
thinvk <= 4.758
– Species properties (Cluster C):
• lifelook=MTG
27%
• sprflow=1
82%
• sumflow=1
76%
• autflow=
65%
• winflow=
80%
• hitecat=2
38%
• aquatic=
95%
• parasite=
100%
• winflow=1
20%
• fleshyf=
96%
• fleshyl=
96%
Description of clusters
•
Description of clusters given in details in excel table
– Site properties
•
•
•
•
twi > 815
max_feb <= 3108
numberspp <= 9
dem>15
– Species properties (Cluster D):
•
•
•
•
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•
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lifelook=H
sprflow=1
sumflow=1
autflow=
winflow=
hitecat=2
aquatic=
parasite=
winflow=1
fleshyf=
fleshyl=
16%
74%
75%
63%
85%
24%
71%
100%
15%
99%
95%
Description of clusters
•
Description of clusters given in details in excel table
– Site properties
•
•
•
•
twi > 815
max_feb <= 3108
numberspp <= 9
dem<=15
– Species properties (Cluster E):
•
•
•
•
•
•
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•
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•
lifelook=H
sprflow=1
sumflow=1
autflow=1
winflow=
hitecat=1
aquatic=
parasite=
winflow=1
fleshyf=
fleshyl=
36%
65%
55%
57%
78%
48%
84%
100%
22%
94%
56%
Description of clusters
• Description of clusters given in details in excel table
– Site properties
• twi <= 815
• dem>1260
– Species properties (Cluster F):
•
•
•
•
•
•
•
•
•
•
•
lifelook=S
sprflow=1
sumflow=1
autflow=
winflow=
hitecat=2
aquatic=
parasite=
winflow=1
fleshyf=
fleshyl=
20%
53%
83%
80%
91%
33%
99%
100%
9%
92%
100%
Description of clusters
• Description of clusters given in details in excel table
– Site properties
• twi <= 815
• dem<=1260
• eff_rain > 19
– Species properties (Cluster G):
•
•
•
•
•
•
•
•
•
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lifelook=T
sprflow=1
sumflow=1
autflow=
winflow=
hitecat=2
aquatic=
parasite=
winflow=1
fleshyf=
fleshyl=
21%
65%
66%
74%
82%
21%
99%
99%
18%
94%
100%
Description of clusters
• Description of clusters given in details in excel table
– Site properties
• twi <= 815
• dem<=1260
• eff_rain <= 19
– Species properties (Cluster H):
•
•
•
•
•
•
•
•
•
•
•
lifelook=T
sprflow=1
sumflow=1
autflow=
winflow=
hitecat=2
aquatic=
parasite=
winflow=1
fleshyf=
fleshyl=
20%
75%
71%
70%
74%
24%
98%
99%
26%
95%
99%