A weighted k-mean clustering algorithm based on singular values with offset clustering centers
Abstract
The
K-means
algorithm
is
widely
used
for
dataset
clustering,
but
it
does
not
consider
the
importance
of
each
attribute
dimension
when
dealing
with
feature
attributes
and
clustering
center
selection,
rather
treats
all
as
having
equal
importance.
In
order
to
solve
this
problem,
paper
proposes
a
weighted
k-mean
(SVW-KMeans)
based
on
singular
values
offset
centers.
calculates
weight
information
data
points
through
value
decomposition
focus
most
significant
different
features,
joining
calculation
optimize
objective
function,
at
same
time,
arithmetic
mean
individuals
center,
shifted
towards
high
so
take
into
full
consideration
features
in
process.
experimental
results
show
that
SVW-KMeans
outperforms
other
algorithms
synthetic
real
datasets,
which
verifies
mainstream
terms
quality
stability.
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Авг. 26, 2024
Язык: Английский