A Comprehensive Overview of Clustering Algorithms in Pattern Recognition

Abstract

Machine learning is a branch of artificial intelligence which recognizes complex patterns for making intelligent decisions based on input data values. In machine learning, pattern recognition assigns input value to given set of data labels. Based on the learning method used to generate the output we have the following classification, supervised and unsupervised learning. Unsupervised learning involves clustering and blind signal separation. Supervised learning is also known as classification. This paper mainly focuses on clustering techniques such as K-means clustering, hierarchical clustering which in turn involves agglomerative and divisive clustering techniques. This paper deals with introduction to machine learning, pattern recognition, clustering techniques. We present steps involved in each of these clustering techniques along with an example and the necessary formula used. The paper discusses the advantages and disadvantages of each of these techniques and in turn we make a comparison of K-means and hierarchical clustering techniques. Based on these comparisons we suggest the best suited clustering technique for the specified application.