Clustering is the process of finding groups of objects such that the objects in a group will be similar to one another and different from the objects in other groups. Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality that corresponds to the intrinsic dimensionality of the data. K-Medoids algorithm for cluster analysis is developed for low dimensional data, often does not work well for high dimensional data like microarray gene expression data and the results may not be accurate in most of the time due to noise and outliers associated with original data. Principal component analysis (PCA) is the best mean-square error sense, and linear dimension reduction technique, is being based on the covariance matrix of the variables, it is a secondorder method. The resulting reduced data set obtained from the application of PCA will be applied to a KMedoids clustering algorithm. A new method to find the initial Medoids makes the algorithm more effective and efficient. This paper shows Amalgamation K-Medoids efficiency is better than K-Medoids. __ampersandsignnbsp;