Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving cluster recovery with feature rescaling factors   The data preprocessing stage is crucial in clustering. Features may describe
entities using different scales. To rectify this, one usually applies feature
normalisation aiming at rescaling features so that none of them overpowers the
others in the objective function of the selected clustering algorithm. In this
paper, we argue that the rescaling procedure should not treat all features
identically. Instead, it should favour the features that are more meaningful
for clustering. With this in mind, we introduce a feature rescaling method that
takes into account the within-cluster degree of relevance of each feature. Our
comprehensive simulation study, carried out on real and synthetic data, with
and without noise features, clearly demonstrates that clustering methods that
use the proposed data normalization strategy clearly outperform those that use
traditional data normalization.

12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics