Circuits and Systems, 2016, 7, 1856-1865 Published Online June 2016 in SciRes. http://www.scirp.org/journal/cs http://dx.doi.org/10.4236/cs.2016.78160 How to cite this paper: Pitchandi, P., Muthukumaravel, S. and Boopathy, S. (2016) Content Based Segregation of Pertinent Documents Using Adaptive Progression. Circuits and Systems, 7, 1856-1865. http://dx.doi.org/10.4236/cs.2016.78160 Content Based Segregation of Pertinent Documents Using Adaptive Progression Perumal Pitchandi, Sreekrishna Muthukumaravel, Suganya Boopathy Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India Received 21 April 2016; accepted 10 May 2016; published 22 June 2016 Copyright © 2016 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Due to the emerging technology era, today a number of firms share their service/product descrip- tions. Such a group of information in the textual form has some structured information, which is beneath the unstructured text. A new attainment which facilitates the form of a structured meta- data by recognizing documents which are likely to have some type and this information is then used for both segregation and search process. The idea of this advent describes some attributes of a text that will match with the query object which acts as identifier both for segregation as well as for storage and retrieval. An adaptive technique is proposed to deal with relevant attributes to annotate a document by satisfying the users querying needs. The solution for annotation-attribute suggestion problem is not based on the probabilistic model or prediction but it is based on the ba- sic keywords that a user can use to query a database to retrieve a document. Experiment results show that Querying value and Content Value approach is much useful in predicting a tag for a document and thus prediction is also based on Querying value and Content value which greatly improves the utility of shared data which is a drawback in the existing system. This approach is different, as we consider only the basic keywords to be matched with the content of a document. When compared with other approaches in the existing system, Clarity is a primary goal as we ex- pect that the annotator may improve the annotations on process. The discovered tags assist on quest of retrieval as an alternative to bookmarking. Keywords Document Annotation, Segregation, Identification, Content Type 1. Introduction Data mining, an interdisciplinary subfield of computer science, is the computational process of discovering pat- terns in large data sets involving methods at the intersection of artificial intellect, contraption erudition information