• 1Sreerama. Kanaka Raghu
• Dr M.Ramakrishna Murthy
• Dr K.Venkata Ramana

Abstract:- In the recent years, the optimization of high-dimensional data contributes vital role in Data engineering applications. In this paper, we addressed a novel population based optimization algorithm called Social Group Optimization (SGO) which is motivation of perception for social behavior of human toward solving a hard problem. In SGO algorithm consists mainly two stages such as improving stage and acquiring stage. In the first stage coordination of people positions based on objective function and in the acquiring stage allowing the people to find the best potential solution for the complex problem under concern. SGO has been simulated for high dimensional benchmark function optimizations and comparison study made among well familiar state – of – art techniques such as Particle Swarm Optimization (PSO) and Teaching– Learning-Based Optimization (TLBO). The outcome of Results clearly shows that SGO more effective and efficient and also its scope is used wide variety of applications.

Clustering, High dimensional data, Fitness Function, TLBO, SGO.

" HIGH DIMENSIONAL CLUSTER DATA PARAMETERS OPTIMIZATION USING SOCIAL GROUP OPTIMIZATION (SGO)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.b660-b666, December-2021, Available :http://www.jetir.org/papers/JETIR2112176.pdf

" HIGH DIMENSIONAL CLUSTER DATA PARAMETERS OPTIMIZATION USING SOCIAL GROUP OPTIMIZATION (SGO)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppb660-b666, December-2021, Available at : http://www.jetir.org/papers/JETIR2112176.pdf