  We present a new approach to component separation in multifrequency CMB
experiments by formulating the problem as that of partitioning the sky into
pixel clusters such that within each pixel cluster the foregrounds have similar
spectrum, using only the information available in the data. Only spectral
information is used for partitioning, allowing spatially far away pixels to
belong to the same cluster if their foreground properties are close. We then
apply a modified internal linear combination method to each pixel cluster.
Since the foregrounds have similar spectrum within each cluster, the number of
components required to describe the foregrounds is smaller compared to all data
taken together and simple pixel based ILC algorithm works extremely well. We
test our algorithm in the full focal plane simulations provided by the Planck
collaboration. We apply our algorithm to the Planck full mission data and
compare our CMB maps with the CMB maps released by the Planck collaboration. We
show that our CMB maps are clean and unbiased on a larger fraction of the sky,
especially at the low Galactic latitudes, compared to publicly available maps
released by the Planck collaboration. This is important for constraining beyond
the simplest $\Lambda$CDM cosmological models and study of anomalies. Our
cleaned CMB maps are made publicly available for use by the cosmology
community.
