Accessing accurate documents by mining auxiliary document information

TL;DR

This paper introduces a two-stage clustering technique that leverages auxiliary document information to enhance the accuracy of document mining and classification.

Contribution

It proposes a novel method combining classical clustering algorithms with auxiliary information to improve document clustering results.

Findings

Improved clustering accuracy over traditional methods

Effective use of auxiliary information in text mining

Two-level abstraction enhances document classification

Abstract

Earlier techniques of text mining included algorithms like k-means, Naive Bayes, SVM which classify and cluster the text document for mining relevant information about the documents. The need for improving the mining techniques has us searching for techniques using the available algorithms. This paper proposes one technique which uses the auxiliary information that is present inside the text documents to improve the mining. This auxiliary information can be a description to the content. This information can be either useful or completely useless for mining. The user should assess the worth of the auxiliary information before considering this technique for text mining. In this paper, a combination of classical clustering algorithms is used to mine the datasets. The algorithm runs in two stages which carry out mining at different levels of abstraction. The clustered documents would then be classified based on the necessary groups. The proposed technique is aimed at improved results of document clustering.