Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Lance-William Clustering Algorithm   One important tool is the optimal clustering of data into useful categories.
Dividing similar objects into a smaller number of clusters is of importance in
many applications. These include search engines, monitoring of academic
performance, biology and wireless networks. We first discuss a number of
clustering methods. We present a parallel algorithm for the efficient
clustering of objects into groups based on their similarity to each other. The
input consists of an n by n distance matrix. This matrix would have a distance
ranking for each pair of objects. The smaller the number, the more similar the
two objects are to each other. We utilize parallel processors to calculate a
hierarchal cluster of these n items based on this matrix. Another advantage of
our method is distribution of the large n by n matrix. We have implemented our
algorithm and have found it to be scalable both in terms of processing speed
and storage.

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The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn