Mining Clustering Dimensions
نویسندگان
چکیده
• Presented an algorithm that learns and helps users visualize important clustering dimensions of a dataset. • Future work involves quantifying the multi-clusterability and ambiguity of a dataset Conclusion and Future Work • Producing the optimal clustering • Spectral clustering, objective function: normalized cut • Optimal partitioning function f: • f=e2, the second eigenvector of the Laplacian • Apply k-means to cluster the data points represented by e2
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