Abstract: A large amount of assorted information are posted and retrieved on web by its users and administrators. For the web users, the main issue is to browse through the exact data they are looking for. This web content mining leads for the developing of an efficient technique for retrieving the exact web contents for users query. Nowadays there are many techniques which provide a good performance in web content mining. These existing techniques could be replaced by improved web content techniques which could be utilized for real world applications. Approximate Membership Extraction (AME) was one among them. Approximate Membership Extraction (AME) provides a full coverage to the true matched substrings from a given document, but many redundancies cause a low efficiency of the AME process and decrease the performance of real world applications using the extracted substrings. As a counter measure to this Approximate Membership Localization (AML) can be used to retrieve true matches for clean references. In this project an AML technique is used for extracting those true matches for clean reference which are non-overlapped. Finally a comparison is made between AME and AML.
Keywords: Approximate Membership Localization (AML), Approximate Membership Extraction (AME), P-Prune Algorithm
Title: An Entity Recognition Framework for Data Mining Techniques
Author: Sruthi Varghese, Nagaraj Naik
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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