A Comparative Analysis of Various Clustering Techniques on Random Datasets

ata Mining is a discovery of knowledge used basically used for finding or exploring the new facts among datasets. It allows the user to find the hidden data among available datasets. Data mining consists of various components including clustering, classification, association rules, sequence analysis etc. Unlabeled data are becoming common and mining such databases becomes more challenging. Clustering is one of the major techniques. In this, user performs mining by searching for similar data. So, in this paper we have enlisted various clustering techniques applied on random datasets and a comprehensive analysis based on time factor i.e. implemented in matlab. Keywords— Clustering, Complete Link, Datasets, Data mining, k-means, DBSCAN.