Database Reference
In-Depth Information
5
35
LT3
DTLA
RT
LT3
DLTA
30
4
25
3
20
15
2
10
1
5
0
0
2
2.5
3
3.5
4
5
10
15
20
25
Neighborhood threshold
Parameter k
(a) Error rate vs. neighborhood.
(b) Error rate vs.
k
.
20
40
RT
LT3
DLTA
RT
LT3
DLTA
35
15
30
25
10
20
15
5
10
5
0
0
5
10
15
20
25
10000
15000
20000
25000
30000
Number of attributes
Number of tuples
(c) Error rate vs. dimensionality.
(d) Error rate vs. cardinality.
Fig. 4.5
Approximation quality of answering top-
k
discriminative typicality queries.
10
LT3
DTLA
50
RT
LT3
DLTA
8
40
6
30
4
20
2
10
0
0
2
2.5
3
3.5
4
5
10
15
20
25
Neighborhood threshold
Parameter k
(a) Error rate vs. neighborhood.
(b) Error rate vs.
k
.
50
RT
LT3
DLTA
50
40
40
30
30
20
20
RT
LT3
DLTA
10
10
0
0
5
10
15
20
25
5000
7500
10000
12500
15000
Number of attributes
Number of tuples
(c) Error rate vs. dimensionality.
(d) Error rate vs. cardinality.
Fig. 4.6
Approximation quality of answering top-
k
representative typicality queries.
tain objects in the data set. We conduct a top-10 discriminative typicality query on
guards. The results are shown in Table 4.5. For comparison, in the same table we
also list the answer to the top-10 simple typicality query on guards. To explain the
results, we list some selected attributes as well. The most discriminatively typical
guards have better performance than those of the highest simple typicality in
3 point
