An Efficient K-Means++ Algorithm using Nearest- Neighbor Search

K-means algorithm is most popular partition based algorithm that is widely used in data clustering. A lot of algorithms have been proposed for data clustering using K-means algorithm due to its simplicity, efficiency and ease convergence. In spite this K-means algorithm has some drawbacks like it scales poorly computationally, initial cluster centers is supplied by the user and stuck in local optima etc. Determining the number of clusters is very complex and is usually done by an expert. Thus, this paper intends to overcome this problem by proposing a parameter-free algorithm for automatic clustering. It is based on successive adequate restarting of K-means algorithm based on Nearest Neighbor search. This proposed approach is more effective and gives a tough competition to the well-known algorithms, X-means and G-means in terms of clustering accuracy and estimation of the correct number of clusters.