The best clustering algorithms in data mining
In data mining, Clustering is the most popular, powerful and commonly used unsupervised learning technique. It is a way of locating similar data objects into clusters based on some similarity. Clustering algorithms can be categorized into seven groups, namely Hierarchical clustering algorithm, Density based clustering algorithm, Partitioning clustering algorithm, Graph based algorithm, Grid based algorithm, Model based clustering algorithm and Combinational clustering algorithm. These clustering algorithms give different result according to the conditions. Some clustering techniques are better for large data set and some gives good result for finding cluster with arbitrary shapes. This paper is planned to learn and relates various data mining clustering algorithms. Algorithms which are under exploration as follows: K Means algorithm, K Medoids, Distributed K Means clustering algorithm, Hierarchical clustering algorithm, Grid based Algorithm and Density based clustering algorithm. This paper compared all these clustering algorithms according to the many factors. After comparison of these clustering algorithms I describe that which clustering algorithms should be used in different conditions for getting the best result.
2016 International Conference on Communication and Signal Processing (ICCSP)