Correlative study and analysis for hidden patterns in text analytics unstructured data using supervised and unsupervised learning techniques

Abstract: Two-third of the data generated by the internet is unstructured text in
the form of e-mails, audio, video, pdf files, word documents, text documents.
Extraction of these unstructured text patterns using mining techniques achieve
quick access to outcomes. Textual data available at online contains different
patterns and when those huge incoming unstructured data enters into the system
creates a problem while organising those documents into meaningful groups.
This paper discusses document classification using supervised learning by
focusing on the concept-based algorithm and also deals with the hiddenpatterns in the documents using unsupervised clustering technique andtopic-based modelling for the analysis and improvement of systematicarrangement of documents by applying k-means and LDA algorithm. Finally,this presents comparative study and importance of clustering than classification
for unstructured documents.