Thesis Abstract and Dissertation Abstracts
Secure Association Rule Mining For Distributed Level Hierarchy in Web
Abstract Category: I.T.
Course / Degree: M.Tech.
Institution / University: Ambedkar Institute of Technology, India
Published in: 2011
Data mining technology can analyze massive data and it play very important role in many domains. If it used improperly it can also cause some new problem of information security. Thus several privacy preserving techniques for association rule mining have also been proposed in the past few years. Association rules are one of the most popular methods of data mining. Various algorithms have been developed for centralized data, while others refer to distributed data scenario. Distributed data Scenarios can also be classified as heterogeneous distributed data and homogenous distributed data and we identify that distributed data could be partitioned as horizontal partition (homogeneous distribution) and vertical partition (heterogeneous distribution). The main goal of dissertation is to secure association rule mining for vertical partition. To reach this goal own extracting algorithm is proposed.
Dissertation Keywords/Search Tags:
Data Mining, Association Rule Mining, Privacy Preserving, Web Log, Vertical Partition
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Submission Details: Dissertation Abstract submitted by Gulshan Shrivastava from India on 21-May-2011 16:03.
Abstract has been viewed 3115 times (since 7 Mar 2010).
Gulshan Shrivastava Contact Details: Email: gulshanstv@gmail.com
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