Efficient Dynamic Clustering Mechanism

Generally, Data Mining or Knowledge Discovery is the procedure of analyzing information from various viewpoints and summary the data for further information Clustering is an unsupervised learning process where it generates an object group based on their similarity in such a way that the objects fit into another group are same and those belonging to other are dissimilar. Initialization of cluster size and iteration is the major problem of data clustering traditional clustering mechanisms like fuzzy C means algorithm and K-Means algorithm. To overcome the problems in data clustering a novel clustering mechanism is proposed in this research named Efficient Dynamic Clustering Mechanism. Our proposed mechanism computes the centroid value using Gaussian distribution and this system computes the new clusters depending on the distance between the data points. Finally, we conduct a set of experiments to prove our proposed Efficient Dynamic Clustering Mechanism is rather than other approaches.