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| An Effective Sub-space Learning for Document Clustering |
| Xianfen Xie, Binhui Wang |
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Abstract Clustering in textual document attracts more and more attention with
the huge Internet news data appears every day. But the performance
can be influenced by the high dimension vectors based on word-bag.
Many of which are redundant information. Hence, it is very necessary
to derive a low dimensional subspace that contain less redundant
information in order to make the documents can be clustered more
reasonably. In traditional studies, learning a subspace and
clustering vectors are divided into two independent steps, e.g.,
feature selection step and clustering step. In this way, it could
not estimate whether the subspace is appropriate for the clustering.
To solve this issue, we select the feature firstly, and combine the
subspace learning step and the clustering step into an iterative
procedure. First, we take into account similarity between the intra
clusters and separability in the inter cluster documents, and then
utilize the affinity propagation to adopt to partition the number of
clusters. The experimental results show that our proposed method
outperforms the conventional methods of document clustering using
our data set.
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| DOI:10.12733/jics20105207 |
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