  Text classification is the process of classifying documents into predefined
categories based on their content. It is the automated assignment of natural
language texts to predefined categories. Text classification is the primary
requirement of text retrieval systems, which retrieve texts in response to a
user query, and text understanding systems, which transform text in some way
such as producing summaries, answering questions or extracting data. Existing
supervised learning algorithms to automatically classify text need sufficient
documents to learn accurately. This paper presents a new algorithm for text
classification using data mining that requires fewer documents for training.
Instead of using words, word relation i.e. association rules from these words
is used to derive feature set from pre-classified text documents. The concept
of Naive Bayes classifier is then used on derived features and finally only a
single concept of Genetic Algorithm has been added for final classification. A
system based on the proposed algorithm has been implemented and tested. The
experimental results show that the proposed system works as a successful text
classifier.
