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

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.