Title: Correlative study and analysis for hidden patterns in text analytics unstructured data using supervised and unsupervised learning techniques
Authors: E. Laxmi Lydia; S. Kannan; S. SumanRajest; S. Satyanarayana
Addresses: Computer Science and Engineering, Vignan's Institute of Information Technology, India ' Citibank, 8 MARINA VIEW, Asia Square Tower 1, 018960, Singapore ' Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, Tamil Nadu, India ' Raghu Engineering College, Visakhapatnam, Andhra Pradesh 531162, India
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.
Keywords: text analytics; concept-based method; data representation; storage; latent Dirichlet allocation; LDA algorithm.
International Journal of Cloud Computing, 2020 Vol.9 No.2/3, pp.150 - 162
Received: 13 Mar 2019
Accepted: 20 Jul 2019
Published online: 24 Aug 2020 *