Implementing K - means Algorithm using Row store and Column store databases : A case study

K-means Clustering is an important algorithm for identifying the structure in data. K-means is the simplest clustering algorithm [8]. This algorithm uses as input a predefined number of clusters i.e., the K from its name. Mean stands for an average, an average location of all the members of a particular cluster. In this work, a novel approach to seeding the clusters with a latent data structure is proposed. This is expected to minimize the need for number of clusters apriory, Time for convergence by providing near optimal cluster centers. Also these algorithms are tested on the latest standards for data warehouse – the column store databases.