Text mining is variance of a field called data mining. To make unstructured data workable by the computer Text mining is used which is also referred as “Text Analytics”. Text categorization, also called as topic spotting is the task of automatically classifies a set of documents into groups from a predefined set. Text classification is an essential application and research topic because of incr...

When we apply binary classification to multi-class classification for text classification, we use the One-Against-All method generally. However, this One-Against-All method has a problem. That is, the documents of a negative set are not labeled manually while those of a positive set are labeled by human. In this paper, we propose that the Sliding Window technique and the EM algorithm are applie...

This paper exposes the results of our participation in the Document Mining track at INEX’07. We have focused on the task of classification of XML documents. Our approach to deal with structured document representations uses classification methods for plain text, applied to flattened versions of the documents, where some of their structural properties have been translated to plain text. We have ...

Automatic classification of text documents has become an important research issue now days. Proper classification of text documents requires information retrieval, machine learning and Natural language processing (NLP) techniques. Our aim is to focus on important approaches to automatic text classification based on machine learning techniques viz. supervised, unsupervised and semi supervised. I...

Classification of text documents presents a unique challenge to conventional classification algorithms. Due to the existence of large number of features in the datasets, providing a desired representation for text documents can be seen as another problem. In this paper a simple but effective representation model for text documents to tackle the classification problem is discussed. Two different...

Text mining is an emerging technology that can be used to augment existing data in corporate databases by making unstructured text data available for analysis. The incredible increase in online documents, which has been mostly due to the expanding internet, has renewed the interest in automated document classification and data mining. The demand for text classification to aid the analysis and m...

In the real world, an operational text classification system is usually placed in the environment where the amount of human-annotated training documents is small in spite of thousands of classes. In this environment text classifier are probably the most appropriate methods for the practical systems rather than other complex learning models. Text classifiers are basically used for free flowing t...

With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, form...