Spectral Clustering, Ordering and Ranking
Statistical Learning with Matrix Factorizations
(Sprache: Englisch)
Spectral Clustering, Ordering and Ranking
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Spectral Clustering, Ordering and Ranking
Klappentext zu „Spectral Clustering, Ordering and Ranking “
Data mining methods are essential for analyzing the ever-growing massive quantities of data. Data clustering is one of the key data mining techniques. In recent years, spectral clustering has been developed as an effective approach to data clustering. This exposition presents recent advances in this new subfield. New concepts are carefully developed and practical examples are extensively utilized to illustrate the ideas. A key feature are the mathematical proofs outlined throughout the text in reasonable detail which highlight the rigorous and principled quality of spectral clustering. A concise introduction to data clustering methods is followed by advanced spectral clustering, ordering and ranking topics which then lead to applications in web and text mining and genomics. An Appendix covering the preliminaries makes this text self-contained. This book is aimed at senior undergraduate and graduate students in computer science, applied mathematics and statistics and researchers and practitioners in machine learning, data mining, multivariate statistics, matrix computation, web analysis, text mining, bioinformatics.Inhaltsverzeichnis zu „Spectral Clustering, Ordering and Ranking “
Part 1 Basic Theory and Algorithms: Introduction- K-means clustering and spectral relaxation
- Spectral graph clustering
- Multi-way spectral clustering
- Bipartite graph clustering
Part 2 Advanced Spectral Clustering and Ordering: Perturbation analysis of spectral clustering
- Green's function
- Random walk and probabilistic spectral clustering
- Semi-definite programming solution for spectral clustering
- Spectral data ordering
Part 3 Related unsupervised learning topics: Spectral ranking
- Further developments on PCA and SVD
- Nonlinear embedding/representation
- Other low-dimension embedding
- Nonnegative matrix representation
- Spectral relaxations for other combinatorial optimization problems
- Level sets based clustering
- Constrained clustering
- subspace clustering
Part 4 Applications in web, text, bioinformatics: Web and text clustering
- Clustering in genomics
- Other applications
- Appendix
Bibliographische Angaben
- Autoren: Chris Ding , Hongyuan Zha
- 2015, 1st Edition., X, 390 Seiten, 20 farbige Abbildungen, 40 Schwarz-Weiß-Abbildungen, Maße: 15,5 x 23,5 cm, Gebunden, Englisch
- Verlag: Springer
- ISBN-10: 0387304487
- ISBN-13: 9780387304489
Sprache:
Englisch
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