Authentication of uncertain data based on k-means clustering

-Data uncertainty is an intrinsic property in different applications such as sensor network monitoring, object recognition, location-based services (LBS), and moving object tracking. The data mining methods are applied to the above mentionedapplications their uncertainty has to be handled to achieve the accurate query results. The several probabilistic algorithm estimates the location and control for each object but not effective in handling query processing in distributed environments. Probabilistic inverted indexing computes the lower bound and upper bound for a threshold keyword but fails to extend the technique on tackle the correlation. In order to overcome the issues in uncertain data mining, Fusion Layer Topological Space Query Indexing technique (FLTS)is introduced. Initially, the queries are articulated on any random subset of attributes in the uncertain data.The FLTS index technique answers the top-k queries competently. FLTS index correctlyshows in their dominant relationships andsignificantly reduces the number of tuple accessed through query processing by pruning redundant tuple based on two criterions such as layer point sort method and the record point sort method. Initially, layer point sort method is used in FLTS indexto sort out tuple totally on the combination of all attribute values of the tuple. Subsequently, each attributes values particularly used for rating the tuple using record point sort method. Therefore,the correlation is removed significantly. Through an analysis of the interaction of the two sorting methods, derive a fixed bound that reduces the number of tuple retrieved during query processing and obtaining the correct query results in distributed environments. An experimental result shows that the FLTS indexing technique improves the query retrieving efficiency, response time, memory consumption and scalability.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Authentication of uncertain data based on k-means clustering

Cited by 2 publications

References 45 publications

Service Quality, Resistance to Change, Employees‟ Engagement, and Outsourcing in Telecom Enterprise Organizations

Service Quality, Resistance to Change, Employees‟ Engagement, and Outsourcing in Telecom Enterprise Organizations

Fusion Layer Topological Space Query Indexing For Uncertain Data Mining

Contact Info

Product

Resources

About