Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Gaussian Measures for Efficient Constraint Based Clustering   In this paper we present a novel iterative multiphase clustering technique
for efficiently clustering high dimensional data points. For this purpose we
implement clustering feature (CF) tree on a real data set and a Gaussian
density distribution constraint on the resultant CF tree. The post processing
by the application of Gaussian density distribution function on the
micro-clusters leads to refinement of the previously formed clusters thus
improving their quality. This algorithm also succeeds in overcoming the
inherent drawbacks of conventional hierarchical methods of clustering like
inability to undo the change made to the dendogram of the data points.
Moreover, the constraint measure applied in the algorithm makes this clustering
technique suitable for need driven data analysis. We provide veracity of our
claim by evaluating our algorithm with other similar clustering algorithms.
Introduction

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The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin