Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Avoiding the dangers of averaging across subjects when using multidimensional scaling
Journal of Mathematical Psychology
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
A tutorial on spectral clustering
Statistics and Computing
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In this paper, we present a novel method for studying the deterioration of renal functions after kidney transplant. We track the kidney functions of 111 patients for 24 months after the kidney transplant and use the time series data to group the patients into four clusters. We have developed two graph-based algorithms for analyzing the data as a pre-processing step prior to the formation of the clusters. The resultant clusters thus formed are statistically analyzed to determine the socio-demographic and clinical factors that may provide insights into the renal functions after the transplants. We also compare the cluster formation against other manifold learning techniques. The quality of the clusters was assessed using the silhouette function. We discuss how our findings can be used for effective intervention strategies.