Allocating power ground vias in 3D ICs for simultaneous power and thermal integrity
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A Topological View of Unsupervised Learning from Noisy Data
SIAM Journal on Computing
Combining co-clustering with noise detection for theme-based summarization
ACM Transactions on Speech and Language Processing (TSLP)
| Hi-index | 0.00 |
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. It starts with well-motivated objective functions; optimization eventually leads to eigenvectors as the solutions, with many clear and interesting algebraic properties. Spectral clustering, ordering and ranking extensively uses matrix-based methods and algorithms. This approach is amenable to vigorous analysis and is benefiting from a treasury of knowledge of linear algebra and applied mathematics accumulated through the centuries. 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.Aconcise introduction todata clusteringmethods is followed by advanced spectral clustering, ordering and rankingtopicswhich then lead toapplications in web andtext mining and genomics. An Appendixcovering thepreliminaries 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.