Micro Clustering Methodology for Document Objects using Deep Learning Techniques
Amirkhan R. Mulla1, Sachin S. Patil2

1Amirkhan R. Mulla, Department of Computer Engineering, Rajarambapu Institute of Technology, Rajaramnar, India.
2Sachin S. Patil, Department of Computer Engineering, Rajarambapu Institute of Technology, Rajaramnar, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1546-1552 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3716129219/2020©BEIESP | DOI: 10.35940/ijeat.B3716.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.
Keywords: Document Classification, NLP, Deep Learning, RNN, Micro Clustering.