Database Reference
In-Depth Information
Figure 2. Examples of similarity queries considering the Euclidean distance. (a) Range selection. (b)
k-Nearest-Neighbor selection considering k=4. (c) Aggregate Range selection. (d) k-Aggregate Nearest
Neighbor selection considering k=1 and g=2. (e) Range join. (f) k-Nearest Neighbors join considering
k=2. (g) k-Closest Neighbors join considering k=3
where δ is a distance function over  j , Q is the set of query centers, s i is a dataset element, and the
power g ∈  is a non-zero real value that we call the grip factor of the similarity aggregation function.
Considering Equation 1, the aggregate range and the aggregate k -nearest neighbor queries applied over
a unitary set Q correspond to the traditional range and k -NN queries respectively.
It is important to note that, different values for the grip factor g can provide interesting different in-
terpretations. For example, g = 1 defines the minimization of the sum of the distances, g = 2 defines the
minimization of the mean square distance (see Figures 2(c) and (d)), and g = ∞ defines the minimization
Search WWH ::




Custom Search