Santosh K. Nanda

Work place: Flytxt Mobile Solutions Pvt. Ltd., 7th Floor, Leela Infopark, Technopark Rd, Technopark Campus, Thiruvananthapuram, Kerala



Research Interests: Computational Science and Engineering, Artificial Intelligence, Pattern Recognition, Image Compression, Image Manipulation, Image Processing, Data Structures and Algorithms, Statistics


Santosh Kumar Nanda is working as Asst. General Manger in Analytics Center of Excellence, (R & D), FLYTXT Mobile Solution Pvt. Ltd., Trivandrum, India.  He completed his PhD from National Institute of Technology, Rourkela. His research interests are Computational Intelligence, Artificial Intelligence, Image Processing Prediction Methodologies, Statistics and Data Science, Mathematical modeling, Pattern Recognition. He has more than 60 research articles in reputed International Journals and International conferences etc. He is now Editor-in-Chief of Journal of Artificial Intelligence, Associate Editor in International Journal of Intelligent System and Application. He is the member of World Federation Soft Computing, USA.

Author Articles
Efficient Intelligent Framework for Selection of Initial Cluster Centers

By Bikram K. Mishra Amiya K. Rath Santosh K. Nanda Ritik R. Baidyanath

DOI:, Pub. Date: 8 Aug. 2019

At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them. When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria. This is because an inefficient center may result in unpredicted outcomes. Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers. We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure. They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices. The clustering accuracy was also conducted using Rand index criteria. All the algorithms computational complexity was analyzed and finally their computation time was also recorded. As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers. More specifically, the accuracy percentage of FECA is higher than the other techniques however, it’s computational complexity and running time is moderately higher.

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