Abstract:
Data Mining involves the use of
sophisticated data analysis tools to discover
previously unknown, valid patterns and
relationships in large data sets. Classification is
one of the most popular data mining tasks with a
wide range of applications and lot of algorithms
have been proposed to build accurate and
scalable classifiers. This paper uses Expectation-
Maximization (EM) algorithm to classify both
labeled and unlabeled data. EM is a class of
iterative algorithms for maximum likelihood or
maximum a posterior estimation in problems with
incomplete data. This system implements EM
algorithm along with the extended definition for
identifying classification of medical disease
provided good classification accuracy based
model.
