Spectral partitioning with multiple eigenvectors
Discrete Applied Mathematics - Special volume on VLSI
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised Learning of Finite Mixture Models
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
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
A tutorial on spectral clustering
Statistics and Computing
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Modern computing with wide range of applications in different areas such as Internet, biology, and social science, involves large scale of data analysis. The relations of data can be modeled as graphs and graph partitioning problem can be effectively approximated by spectral approaches. A critically important problem in graph partition is determination of the cluster number k. Although eigengap heuristic is a principle for this problem and is supported by theory, it is difficult to be applied for the real-world data and complex graphs. In this paper, by considering the general data analysis scenario, we present an algorithm to determine the cluster number k and perform clustering task simultaneously. The experimental result shows that our algorithm works successfully even for the real world data, which is therefore a promising tool for future data analysis.