Karteeka Pavan. Kanadam

Work place: Department of Computer Applications, RVR & JC College of Engineering, Guntur, India

E-mail: kanadamkarteeka@gmail.com


Research Interests: Pattern Recognition, Data Structures and Algorithms, Mathematics of Computing


Dr. Karteeka Pavan. Kanadam This author has received her Ph.D., in Computer Science and Engineering from Acharya Nagarjuna University, Guntur in 2011. Earlier in 1996, she had completed her Master’s degree in Computer Applications at Andhra University, Visakhapatnam.

She is presently working as Professor in Department of Computer Applications at RVR &JC College of Engineering, Guntur.

Prof. Kanadam is having 21 years of teaching experience and published more than 28 research publications in reputed International Journals and Conferences. She is an life member of CSI and ISTE and her research interest includes Soft Computing, Bioinformatics, Data mining, and Pattern Recognition.

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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