Selecting Optimal Weighted Medoids for Clustering

B. Vara Prasada Rao; M. Sreelatha; M. Shashi

Selecting Optimal Weighted Medoids for Clustering

B. Vara Prasada Rao ¹, M. Sreelatha ¹, M. Shashi ²

Affiliations
1 Department of Computer Science & Engineering, R.V.R. & J.C. College of Engineering, Chowdavaram, Guntur, A.P., India
2 Department of Computer Science & System Engineering, Andhra University College of Engineering, Andhra University, Visakhapatnam, A.P., India

Clustering is the process of grouping data into clusters. Partitioning is an important clustering method. K-medoids is a classical partitioning method. K-medoids generates k clusters for a dataset of n objects using distance measure. But this algorithm may not be suitable for many real life applications. In addition to distance measure, the medoid selection may depend on many other factors in real life. A new weighted k-medoids algorithm is proposed in this paper to find optimal medoids using a new measure, weights of the medoids.