Ramachandra Rao. Kurada

Work place: Dept. of CSE, Shri Vishnu Engineering College for Women, Bhimavaram, India

E-mail: ramachandrarao.kurada@gmail.com


Research Interests: Computer systems and computational processes, Artificial Intelligence, Computer Architecture and Organization, Data Structures and Algorithms


Ramachandra Rao. Kurada This author has completed his M.Tech CSE in SSAIST, Surampalem, A.P., in 2012. Earlier, in 1999 he has completed his Master’s degree in Computer Science at VSM College, Ramachandrapuram.  

He is currently working as Asst. Prof. in Department of Computer Science and Engineering at Shri Vishnu Engineering College for Women, Bhimavaram. He has 17 years of teaching experience and is pursuing his part-time Ph.D. in CSE at Acharya Nagarjuna University, Guntur, under the esteemed guidance of Dr. Karteeka Pavan Kanadam.

Mr. Kurada is a life member of CSI, IEA and ISTE. His research interests are Computational Intelligence, Data warehouse & Mining, Bioinformatics, Networking and Securities.

Author Articles
A Novel Evolutionary Automatic Data Clustering Algorithm using Teaching-Learning-Based Optimization

By Ramachandra Rao. Kurada Karteeka Pavan. Kanadam

DOI: https://doi.org/10.5815/ijisa.2018.05.07, Pub. Date: 8 May 2018

Teaching-Learning-Based Optimization (TLBO) is a contemporary algorithm being used as a novel, trustworthy, precise and robust optimization technique for global optimization over continuous spaces both constrained and unconstrained tribulations. TLBO works on the beliefs of teaching and learning and clearly justifies this pedagogy by highlighting the effect of power of a teacher on the output of learners in a class. This paper, explores the applicability of k-means unsupervised learning into TLBO with two endeavors, i.e. to automatically find the optimal number of naturally classified partition in the data without any prior information, and the other is to inspect the naturally classified partitions with cluster validity indices (CVIs) and endorse the goodness of clusters. The proposed automatic clustering algorithm using TLBO (AutoTLBO) pursues a novel evolutionary approach by incorporating the simple k-means algorithm and CVIs into TLBO to configure and validate automatic natural partition in datasets. This algorithm retains the core ideology of clustering to minimize the inter cluster distances and maximize the intra cluster distances among the data. Experimental analysis substantiates the openness of the anticipated method after inspecting suavest panoramic rendering over artificial and benchmark datasets.

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