Distance based k-means clustering algorithm for determining number of clusters for high dimensional data
M.C. Alibuhtto, Nor Idayu Mahat · Decision Science Letters · 2019
Clustering is one of the most common unsupervised data mining classification techniques for splitting objects into a set of meaningful groups.However, the traditional k-means algorithm is not applicable to retrieve useful information / clusters, particularly when there is an overwhelming growth of multidimensional data.Therefore, it is necessary to introduce a new strategy to determine the optimal number of clusters.To improve the clustering task on high dimensional data sets, the distance based k-means algorithm is proposed.The proposed algorithm is tested using eighteen sets of normal and non-normal multivariate simulation data under various combinations.Evidence gathered from the simulation reveal that the proposed algorithm is capable of identifying the exact number of clusters..