Performance Analysis of Subspace Clustering Algorithms in Biological Data

Abstract: Data clustering which is also called as Cluster Analysis is the unsupervised classification of data into various clusters. Clustering is a method of unsupervised learning which is generally implemented by various machine learning techniques. In this paper a specific comparison of three kinds of clustering is introduced and finally the cost function and loss function are calculated. For evaluating any clustering methods, calculation of the error percentage of the concerned method play an important factor. In this paper comparison is carried out on k-means clustering algorithms, hierarchical algorithms and density based algorithms. The main criteria where focus is given while comparing the clustering algorithms are: Scalability, Classes for dealing with noise and extra deposition, different dimensions of high levels etc.

Keywords: Clustering, K means, Hierarchical algorithms, Density based algorithms, Cluster.