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Extended sub tree: a new similarity function for tree struc PowerPoint Presentation
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Extended sub tree: a new similarity function for tree struc

Extended sub tree: a new similarity function for tree struc

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Extended sub tree: a new similarity function for tree struc

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  1. Artificial IntelligencebasedAutomaticHierarchialStructureDecisionmaker IEEE transactions on knowledge and Data Engineering, vol. 26, no. 4, april2014 Extended sub tree: a new similarity function for tree structure

  2. A Software /Manufacturing Research Company Run By Microsoft Most Valuable Professional VenkatesanPrabu .J MANAGING DIRECTOR Microsoft Web Developer Advisory Council team member and a well known Microsoft Most Valuable Professional (MVP) for the year 2008, 2009, 2010,2011,2012,2013 ,2014. LakshmiNarayanan.J GENERAL MANAGER BlackBerry Server Admin. Oracle 10g SQL Expert. Arunachalam.J Electronic Architect Human Resourse Manager

  3. Abstract • Althoughseveraldistanceorsimilarityfunctionsfortreeshavebeenintroduced, their performance isnotalwayssatisfactory in manyapplications, rangingfromdocumentclusteringto natural languageprocessing. Thisresearchproposes a new similarityfunctionfortrees, namely Extended Subtree (EST),. • wherea new subtreemappingisproposed. EST generalizestheedit base distancesbyproviding new rules forsubtreemapping. Further, the new approachseekstoresolvetheproblems and limitations of previousapproaches. Extensiveevaluationframeworks are developedtoevaluatethe performance of the new approachagainstpreviousproposals. • Clusteringand classification case studiesutilizingthree real-world and onesyntheticlabeled data sets are performedtoprovideanunbiasedevaluationwheredifferentdistancefunctions are investigated.

  4. Existing System • Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering are divided into two categories such as • Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. • Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. • A hierarchical of an organization is considered in the existing system, the acquaint problem in the hierarchical level has been identified by the means of Heuristic Predecessor Based AI Mechanism. • An automatic system has been developed to provide the suggestions for the acquired problem in the hierarchical structure.

  5. Proposed System • In the projected system, an automated system of restructuring the hierarchy with decision making model has been made. • Initially, an unframed hierarchical design of an organization is inputted in the system, the problem in the hierarchical structure is identified by standard design format comparison. • The concept of artificial intelligence is utilized in the projected system in which the unframed system are acquired into decision making process. • An automatic system has been developed to provide the suggestions for the acquired problem in the hierarchical structure of the organization and the structure has been reframed.

  6. System Requirement • HARDWARE REQUIREMENT: Processor : Core 2 duo Speed : 2.2GHZ RAM : 2GB Hard Disk : 160GB • SOFTWARE REQUIREMENT: Platform : DOTNET (VS2010) , ASP.NET Dot net. Database : SQL Server 2008 R2

  7. Arhitecture Diagram

  8. Records Breaks Asia Book Of Records Tamil Nadu Of Records India Of Records MVP Awards World Record

  9. Services: A Software /Manufacturing Research Company Run By Microsoft Most Valuable Professional Inplant Training. Internship. Workshop’s. Final Year Project’s. Industrial Visit. Contact Us: +91 98406 78906,+91 90037 18877 kaashiv.info@gmail.com www.kaashivinfotech.com Shivanantha Building (Second building to Ayyappan Temple),X41, 5th Floor, 2nd avenue,Anna Nagar,Chennai-40.