Paper Title
Identifying Scalable Data and Increasing Efficiency of Spectral Clustering

The spectral clustering algorithm is an algorithm for placing N data points in an I-dimensional space into different clusters. Each cluster is described by its similarity, which means that the points in the same cluster are similar and points in different clusters are dissimilar to each other. Recently, spectral clustering has become an increasingly espouse tool and has been applied in many areas such as statistics, machine learning, pattern recognition, data mining, and image processing. This survey paper discusses these methods in detail and later on introduces the relationship between spectral clustering and k-means clustering and spectral clustering's applications in image segmentation, educational data mining, entity resolution and speech separation. It also mention the improvements in this algorithm using Nystrom methods.