Text Mining On A Grid Environment
Author(s)
V. G. Roncero, M. C. A. Costa & N. F. F. Ebecken
Abstract
The enormous amount of information stored in unstructured texts cannot simply
be used for further processing by computers, which typically handle text as
simple sequences of character strings. Text mining is the process of extracting
interesting information and knowledge from unstructured text. One key difficulty
with text classification learning algorithms is that they require many
hand-labeled documents to learn accurately. In the text mining pattern discovery
phase, the text classification step aims to automatically attribute one or more
pre-defined classes to text documents. In this research, we propose to use an
algorithm for learning from labeled and unlabeled documents based on the
combination of Expectation-Maximization (EM) and a naïve Bayes classifier on
a grid environment, this combination is based on a mixture of multinomials,
which is commonly used in text classification. Naïve Bayes is a probabilistic
approach to inductive learning. It estimates the a posteriori probability that a
document belongs to a class given the observed feature values of the document,
assuming independence of the features. The class with the maximum a posteriori
probability is assigned to the document. EM is a class of iterative algorithms for
maximum likelihood or maximum a posteriori estimation in problems with
unlabeled data. The grid environment is a geographically distributed
computation infrastructure composed of a set of heterogeneous resources. Text
classification mining methods are time-consuming, but using the grid
infrastructure can bring significant benefits in the learning and classification
process.
Keywords: grid computing, text classification, Expectation-Maximization, naïve
Bayes.
Keywords
grid computing, text classification, Expectation-Maximization, naïve
Bayes.
Related Book
Other papers in this volume
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