Clustering techniques are often applied in data analytics for interpreting the similarities within data objects over large datasets. Despite the existence of many clustering algorithm in the literature such as connectivity, centroid, distribution, density etc, the factor that constitutes a cluster are different from one another. However, the success of clustering depends upon the maximization of intra-cluster similarity and inter-cluster dissimilarity. The significant implication of clustering algorithms in many real-world applications emerges the proposal of newer algorithms. As a consequence, in this paper, a novel effort is made to generate clusters from a different aspect of grouping data objects using multidimensional geographical linear equation called Equilin Clustering. The technique incorporates the standard linear equation and the method of percentage split for clustering numerical data. The results show that the performance of Equilin Clustering yields better cluster results with reduced complexity over time and number of iterations.

Keywords: linear equation; clustering; percentage split; Euclidean distance