  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.
