Future of Information and Communication Conference (FICC) 2024
4-5 April 2024
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 2 Issue 6, 2011.
Abstract: Clustering is the process of classifying objects into different groups by partitioning sets of data into a series of subsets called clusters. Clustering has taken its roots from algorithms like k-medoids and k-medoids. However conventional k-medoids clustering algorithm suffers from many limitations. Firstly, it needs to have prior knowledge about the number of cluster parameter k. Secondly, it also initially needs to make random selection of k representative objects and if these initial k medoids are not selected properly then natural cluster may not be obtained. Thirdly, it is also sensitive to the order of input dataset.Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The database can be clustered in many ways depending on the clustering algorithm employed, parameter settings used, and other factors. Multiple clustering can be combined so that the final partitioning of data provides better clustering. In this paper, an efficient density based k-medoids clustering algorithm has been proposed to overcome the drawbacks of DBSCAN and kmedoids clustering algorithms. The result will be an improved version of kmedoids clustering algorithm. This algorithm will perform better than DBSCAN while handling clusters of circularly distributed data points and slightly overlapped clusters.
Raghuvira Pratap A, K Suvarna Vani, J Rama Devi and Dr.K Nageswara Rao, “An Efficient Density based Improved K- Medoids Clustering algorithm ” International Journal of Advanced Computer Science and Applications(IJACSA), 2(6), 2011. http://dx.doi.org/10.14569/IJACSA.2011.020607
@article{A2011,
title = {An Efficient Density based Improved K- Medoids Clustering algorithm
},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.020607},
url = {http://dx.doi.org/10.14569/IJACSA.2011.020607},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {6},
author = {Raghuvira Pratap A and K Suvarna Vani and J Rama Devi and Dr.K Nageswara Rao}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.