Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A spectral method to separate disconnected and nearly-disconnected web graph components
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Unsupervised Learning: Self-aggregation in Scaled Principal Component Space
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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We present a coherent framework for data clustering. Starting with a Hopfield network, we show the solutions for several well-motivated clustering objective functions are principal components. For MinMaxCut objectives motivated for ensuring cluster balance, the solutions are the nonlinearly scaled principal components. Using scaled PC A, we generalize to multi-way clustering, constructing a self-aggregation network, where connection weights between different clusters are automatically suppressed while connection weights within same clusters are automatically enhanced.