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A Cross Validation Architecture for the Clustering Using Feed Forward Neural Network

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Data Clustering is a serious issue and wrong placement of data files leads to a major flaw in the searching document files. The clustering between the documents are always based on some similarity in between the data files. This research paper utilizes cosine similarity as the co relation calculator and then a threshold based grouping is implemented. The proposed work utilizes Feed Forward Neural Network for the cross validation of the clustered elements. The proposed work is evaluated using Mean Square Error and False Placed Elements in the cluster mechanism. An improvement of 25% is noticed for the proposed work.
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Keywords: Clustering; Cross Validation; Similarity Value

Document Type: Research Article

Affiliations: M.M. Institute of Computer Technology & Business Management, Maharishi Markandeshwar (Deemed to be University), Mullana 133207, Ambala, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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