Comparative Study of Various Clustering Techniques

Abstract

Today is the time of machine learning. And here we are going to study about clustering. Clustering
is a learning technique to get the information from a huge amount of data set. In this technique
multiple points are grouped together based on their similarity. Data points in one cluster are
having more similarities than the similarities between data points of any another cluster.
Clustering is a technique in which an object belongs to a group or cluster of same type of objects.
That means clustering focus on intra group similarity rather than inter group similarity. Clustering
is an unsupervised learning technique. Aim is to organize data based on their pattern and similarity
measure. This paper consist of various types of clustering methods and their pros and cons. These
techniques are divided into multipleparts: 1) Partitioning clustering algorithm, 2) Hierarchical
clustering algorithm 3)Density-based clustering algorithm.We can say Hierarchical Clustering isa
better than any other techniques.