An optimized survey of clustering algorithms

Computer Engineering

The goal of this survey is to provide a comprehensive review and comparison of different clustering techniques in data mining. Clustering is a significant task in data analysis and data mining applications. It is the task of arranging a set of objects so that objects in the identical group are more related to each other than to those in other groups (clusters). Mining can be done by using supervised learning and unsupervised learning. The clustering is unsupervised learning. A good clustering method will produce high superiority clusters with high intra-class similarity and low inter-class similarity. Clustering algorithms can be categorized into partition-based algorithms like K-Means clustering, hierarchical-based algorithms and density-based algorithms like DBSCAN. Partitioning clustering algorithm splits the data points into k partition, where each partition represents a cluster. Partitioning is the centroid based clustering; the value of k-mean is specified priori. Hierarchical clustering is a technique of clustering. It divides the same dataset by constructing a hierarchy of clusters. Density based algorithm (DBSCAN) find the cluster according to the regions which grow with high density. In this survey paper, an analysis of clustering and its different techniques in data mining are studied.

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