Text Classification using the Concept of Association Rule of Data Mining

TL;DR

This paper proposes a novel text classification method that combines association rule mining to derive features from pre-classified texts with Naive Bayes for final classification, aiming to reduce manual effort and cost.

Contribution

It introduces a new approach integrating association rule mining with Naive Bayes to improve text classification efficiency and reduce manual feature selection.

Findings

Effective feature extraction using association rules

Improved classification accuracy over baseline methods

Reduced manual effort in feature selection

Abstract

As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper we will discuss a procedure of classifying text using the concept of association rule of data mining. Association rule mining technique has been used to derive feature set from pre-classified text documents. Naive Bayes classifier is then used on derived features for final classification.