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Ref: 06857
Spectral Relational Clustering
Spectral Relational Clustering, Multi-type Relational Data
A novel general algorithm to cluster multi-type interrelated data objects simultaneously by iteratively embedding each type of data objects into low dimensional spaces.
About
This invention is based on a novel general model, the collective factorization on related matrices, to discover the hidden structures of multi-types of objects based on both feature information and relation information. By clustering the multi-types of objects simultaneously, the model performs adaptive dimensionality reduction for each type of data. The algorithm has the simplicity of spectral clustering approaches but at the same time also applicable to relational data with various structures. Theoretic analysis and experimental results demonstrate the promise and effectiveness of the algorithm.
Key Benefits
It deals with the problem of high dimensionality more efficiently. It deals with the problem of sparsity more efficiently. Unlike the existing technologies which are applicable to only the relational data with special structures, it is applicable to relational data with various structures. Unlike the traditional approaches that provide only local hidden structures, it provides both local and global hidden structures.