Parallel Comparison of Text Document with Input Data Mining and VizSFP

Abstract- This paper present that increasing efficiency for document processing is fundamental concept for any organization. All the documents have been processed manually but it seems very difficult if someone needs to have particular information from particular document in a less time. Therefore, parallel comparison is focusing on performance and efficient processing of multiple documents simultaneously. Design of parallel algorithm and performance measurement is the major issue. If one wants the document content to be excess as soon as possible then it require too much time. The major need of this paper is to meet the performance objectives such as time, dataset names, and size of data sets, support value and match score that tell us the whole information about the particular document and also give us the document in the re-rank manner and visualizes this result for easy analysis. There is no any Technique that can handle, manage and retrieve the information as per the user need. So, we used the combination of the clustering and mining technique to prove the result of this parallel comparison and to evaluate the accuracy i.e. stable output in the form of graph. The experimental results show that the proposed parallel comparison algorithm for each Mining, Clustering and for Comparison achieves good performance as compare to sequential