Abstract: Cluster analysis enclosed a number of different algorithms and methods for grouping objects into a respective cluster using the similarity among objects. The attractiveness of cluster analysis is its ability to find groups or clusters directly from the given data. Many clustering approaches and algorithms have been developed and successfully applied to many applications. Spectral clustering groups the objects with high similarity measure and eigenvalues. This paper gives an overview of the various types of clustering and the research conclusion from the recent techniques. The analysis of the various application involved with the spectral clustering is studied with its problem analysis.
Keywords: Consensus Clustering, Ensemble Clustering, Spectral Clustering, Co-association Matrix, Weighted K-means.