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 hidden patterns in the documents using unsupervised clustering technique and topic-based modelling for the analysis and improvement of systematic arrangement of documents by applying k-means and LDA algorithm. Finally, this presents comparative study and importance of clustering than classification for unstructured documents.