Document Classification Using Expectation Maximization with Semi Supervised Learning

As the amount of online document increases, the demand for document classification to aid the analysisand management of document is increasing. Text is cheap, but information, in the form of knowing whatclasses a document belongs to, is expensive. The main purpose of this paper is to explain the expectationmaximization technique of data mining to classify the document and to learn how to improve the accuracywhile using semi-supervised approach. Expectation maximization algorithm is applied with both supervisedand semi-supervised approach. It is found that semi-supervised approach is more accurate and effective.The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”.The algorithm first trains a classifier using the labeled document and probabilistically classifies theunlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository datasetin which some changes have been done from our side.