Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Analysis of Enhanced Clustering Algorithm for Gene
  Expression Data   Microarrays are made it possible to simultaneously monitor the expression
profiles of thousands of genes under various experimental conditions. It is
used to identify the co-expressed genes in specific cells or tissues that are
actively used to make proteins. This method is used to analysis the gene
expression, an important task in bioinformatics research. Cluster analysis of
gene expression data has proved to be a useful tool for identifying
co-expressed genes, biologically relevant groupings of genes and samples. In
this paper we applied K-Means with Automatic Generations of Merge Factor for
ISODATA- AGMFI. Though AGMFI has been applied for clustering of Gene Expression
Data, this proposed Enhanced Automatic Generations of Merge Factor for ISODATA-
EAGMFI Algorithms overcome the drawbacks of AGMFI in terms of specifying the
optimal number of clusters and initialization of good cluster centroids.
Experimental results on Gene Expression Data show that the proposed EAGMFI
algorithms could identify compact clusters with perform well in terms of the
Silhouette Coefficients cluster measure.

23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph