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ument Clustering Based On Non-Negative Matrix Factorization文档的基于非负矩阵分解的聚类.ppt

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ument Clustering Based On Non-Negative Matrix Factorization文档的基于非负矩阵分解的聚类

A General Model for Relational Clustering Bo Long and Zhongfei (Mark) Zhang Computer Science Dept./Watson School SUNY Binghamton Xiaoyun Wu Yahoo! Inc. Philip S.Yu IBM Watson Research Center Multi-type Relational Data (MTRD) is Everywhere! Bibliometrics Papers, authors, journals Social networks People, institutions, friendship links Biological data Genes, proteins, conditions Corporate databases Customers, products, suppliers, shareholders Challenges for Clustering! Data objects are not identically distributed: Heterogeneous data objects (papers, authors). Data objects are not independent Heterogeneous data objects are related to each other. Relational Data? Flat Data? Relational Data? Flat Data? No interactions of hidden structures of different types of data objects Difficult to discover the global community structure. A General Model: Collective Factorization on Related Matrices Formulate multi-type relational data as a set of related matrices; cluster different types of objects simultaneously by factorizing the related matrices simultaneously. Make use of the interaction of hidden structures of different types of objects. Data Representation Represent a MTRD set as a set of related matrices: Relation matrix, R(ij), denotes the relations between ith type of objects and jth type of objects. Feature matrix, F(i), denotes the feature values for ith type of objects. Matrix Factorization Model: Collective Factorization on Related Matrices (CFRM) CFRM Model: Example Spectral Clustering Algorithms that cluster points using eigenvectors of matrices derived from the data Obtain data representation in the low-dimensional space that can be easily clustered Traditional spectral clustering focuses on homogeneous data Main Theorem: Algorithm Derivation: Iterative Updating Spectral Relaxation Spectral Relational Clustering (SRC) Spectral Relational Clustering: Example Update C (1) as k1 leading eigenvectors of Update C (2) as k2 leading eigenvectors of Upda