<s>
In	O
computer	B-General_Concept
science	I-General_Concept
,	O
data	B-Algorithm
stream	I-Algorithm
clustering	I-Algorithm
is	O
defined	O
as	O
the	O
clustering	B-Algorithm
of	O
data	O
that	O
arrive	O
continuously	O
such	O
as	O
telephone	O
records	O
,	O
multimedia	O
data	O
,	O
financial	O
transactions	O
etc	O
.	O
</s>
<s>
Data	B-Algorithm
stream	I-Algorithm
clustering	I-Algorithm
is	O
usually	O
studied	O
as	O
a	O
streaming	O
algorithm	O
and	O
the	O
objective	O
is	O
,	O
given	O
a	O
sequence	O
of	O
points	O
,	O
to	O
construct	O
a	O
good	O
clustering	B-Algorithm
of	O
the	O
stream	O
,	O
using	O
a	O
small	O
amount	O
of	O
memory	O
and	O
time	O
.	O
</s>
<s>
Data	B-Algorithm
stream	I-Algorithm
clustering	I-Algorithm
has	O
recently	O
attracted	O
attention	O
for	O
emerging	O
applications	O
that	O
involve	O
large	O
amounts	O
of	O
streaming	O
data	O
.	O
</s>
<s>
For	O
clustering	B-Algorithm
,	O
k-means	B-Algorithm
is	O
a	O
widely	O
used	O
heuristic	O
but	O
alternate	O
algorithms	O
have	O
also	O
been	O
developed	O
such	O
as	O
k-medoids	B-Algorithm
,	O
CURE	B-Algorithm
and	O
the	O
popular	O
BIRCH	B-Algorithm
.	O
</s>
<s>
The	O
problem	O
of	O
data	B-Algorithm
stream	I-Algorithm
clustering	I-Algorithm
is	O
defined	O
as	O
:	O
</s>
<s>
STREAM	O
is	O
an	O
algorithm	O
for	O
clustering	B-Algorithm
data	O
streams	O
described	O
by	O
Guha	O
,	O
Mishra	O
,	O
Motwani	O
and	O
O'Callaghan	O
which	O
achieves	O
a	O
constant	B-Algorithm
factor	I-Algorithm
approximation	I-Algorithm
for	O
the	O
k-Median	O
problem	O
in	O
a	O
single	O
pass	O
and	O
using	O
small	O
space	O
.	O
</s>
<s>
To	O
understand	O
STREAM	O
,	O
the	O
first	O
step	O
is	O
to	O
show	O
that	O
clustering	B-Algorithm
can	O
take	O
place	O
in	O
small	O
space	O
(	O
not	O
caring	O
about	O
the	O
number	O
of	O
passes	O
)	O
.	O
</s>
<s>
Small-Space	O
is	O
a	O
divide-and-conquer	B-Algorithm
algorithm	I-Algorithm
that	O
divides	O
the	O
data	O
,	O
S	O
,	O
into	O
pieces	O
,	O
clusters	O
each	O
one	O
of	O
them	O
(	O
using	O
k-means	B-Algorithm
)	O
and	O
then	O
clusters	O
the	O
centers	O
obtained	O
.	O
</s>
<s>
We	O
can	O
also	O
generalize	O
Small-Space	O
so	O
that	O
it	O
recursively	O
calls	O
itself	O
i	O
times	O
on	O
a	O
successively	O
smaller	O
set	O
of	O
weighted	O
centers	O
and	O
achieves	O
a	O
constant	B-Algorithm
factor	I-Algorithm
approximation	I-Algorithm
to	O
the	O
k-median	O
problem	O
.	O
</s>
<s>
Other	O
well-known	O
algorithms	O
used	O
for	O
data	B-Algorithm
stream	I-Algorithm
clustering	I-Algorithm
are	O
:	O
</s>
<s>
BIRCH	B-Algorithm
:	O
builds	O
a	O
hierarchical	O
data	O
structure	O
to	O
incrementally	O
cluster	O
the	O
incoming	O
points	O
using	O
the	O
available	O
memory	O
and	O
minimizing	O
the	O
amount	O
of	O
I/O	O
required	O
.	O
</s>
<s>
The	O
complexity	O
of	O
the	O
algorithm	O
is	O
since	O
one	O
pass	O
suffices	O
to	O
get	O
a	O
good	O
clustering	B-Algorithm
(	O
though	O
,	O
results	O
can	O
be	O
improved	O
by	O
allowing	O
several	O
passes	O
)	O
.	O
</s>
<s>
COBWEB	B-Algorithm
:	O
is	O
an	O
incremental	O
clustering	B-Algorithm
technique	O
that	O
keeps	O
a	O
hierarchical	B-Algorithm
clustering	I-Algorithm
model	O
in	O
the	O
form	O
of	O
a	O
classification	B-Algorithm
tree	I-Algorithm
.	O
</s>
<s>
For	O
each	O
new	O
point	O
COBWEB	B-Algorithm
descends	O
the	O
tree	O
,	O
updates	O
the	O
nodes	O
along	O
the	O
way	O
and	O
looks	O
for	O
the	O
best	O
node	O
to	O
put	O
the	O
point	O
on	O
(	O
using	O
a	O
category	B-General_Concept
utility	I-General_Concept
function	I-General_Concept
)	O
.	O
</s>
<s>
C2ICM	O
:	O
builds	O
a	O
flat	O
partitioning	O
clustering	B-Algorithm
structure	O
by	O
selecting	O
some	O
objects	O
as	O
cluster	O
seeds/initiators	O
and	O
a	O
non-seed	O
is	O
assigned	O
to	O
the	O
seed	O
that	O
provides	O
the	O
highest	O
coverage	O
,	O
addition	O
of	O
new	O
objects	O
can	O
introduce	O
new	O
seeds	O
and	O
falsify	O
some	O
existing	O
old	O
seeds	O
,	O
during	O
incremental	O
clustering	B-Algorithm
new	O
objects	O
and	O
the	O
members	O
of	O
the	O
falsified	O
clusters	O
are	O
assigned	O
to	O
one	O
of	O
the	O
existing	O
new/old	O
seeds	O
.	O
</s>
<s>
CluStream	O
:	O
uses	O
micro-clusters	O
that	O
are	O
temporal	O
extensions	O
of	O
BIRCH	B-Algorithm
cluster	O
feature	O
vector	O
,	O
so	O
that	O
it	O
can	O
decide	O
if	O
a	O
micro-cluster	O
can	O
be	O
newly	O
created	O
,	O
merged	O
or	O
forgotten	O
based	O
in	O
the	O
analysis	O
of	O
the	O
squared	O
and	O
linear	O
sum	O
of	O
the	O
current	O
micro-clusters	O
data-points	O
and	O
timestamps	O
,	O
and	O
then	O
at	O
any	O
point	O
in	O
time	O
one	O
can	O
generate	O
macro-clusters	O
by	O
clustering	B-Algorithm
these	O
micro-clustering	O
using	O
an	O
offline	O
clustering	B-Algorithm
algorithm	I-Algorithm
like	O
K-Means	B-Algorithm
,	O
thus	O
producing	O
a	O
final	O
clustering	B-Algorithm
result	O
.	O
</s>
