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Unsupervised Cluster Matching for Content Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 20 |
| Year of Publication: 2020 |
| Authors: E. Suchitha, N. Venkata Subba Reddy, Prasanta Kumar Sahoo |
10.5120/ijca2020920176
|
E. Suchitha, N. Venkata Subba Reddy, Prasanta Kumar Sahoo . Unsupervised Cluster Matching for Content Model. International Journal of Computer Applications. 176, 20 ( May 2020), 39-41. DOI=10.5120/ijca2020920176
Abstract
People are generating huge amount of data which user need to store data in the storage devices. But storing the data in cloud is unsecure and people are storing the same data again and again. to avoid waste of storing the data again and again on same documents, we are going to use clustering the data documents for same documents and transform them into single language and store them. Whenever user need the document then it translate into user defined language and shows results to user. The application of document clustering can be categorized to two types, online and offline. Online applications are usually constrained by efficiency problems when compared to offline applications. Text clustering may be used for different tasks, such as grouping similar documents (news, tweets, etc.) and the analysis of customer/employee feedback, discovering meaningful implicit subjects across all documents. Documents can be clustered based on structure or content based meaning of the documents. In the existing system when documents are translated into user defined language documents are getting different meaning and it can convert into only two languages only.
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