
Clustering from Data Streams 89
Algorithm 13: Algorithm for Fractal Clustering: Incremental Step.
input : S: A Set of Examples that fit in memory
begin
foreach P ∈ S do
foreach i ∈ {1, . . . , k} do
C
0
i
← C
i
S
{p}
Compute F
d
(C
0
i
)
Find
ˆ
i = min
i
(|F
d
(C
0
i
) − F
d
(C
i
)|)
if |F
d
(C
0
ˆ
i
) − F
d
(C
ˆ
i
)| > τ then
Discard p as noise
else
Place p in cluster C
ˆ
i
end
Algorithm 14: Algorithm for Fractal Clustering: Tracking Cluster
Changes.
input : S: A Set of Examples that fit in memory
begin
Initialize the count of successfully clustered points: r = 0
foreach P ∈ S do
Use FC to cluster the point
if P is not an outlier then
r ← r + 1
s ←
3(1+)
2
× ln(2/δ)
if r ≤ s then
Initialize clusters using ...