A01=David Skillicorn
Adjacency Matrix
advanced data analysis
Author_David Skillicorn
Bipartite Graph
Blind Source Separation Problem
Category=PBT
Category=UB
Category=UN
Category=UNF
Category=UY
clustering
collaborative filtering
comlpex data sets
Core Matrix
Dataset Matrix
David Skillicorn
decomposition
decompositions
dimensional
dimensionality reduction
Dot Product
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Gradient Descent Conjugate
graph analysis
hierarchical
Hierarchical Clustering
high
Hold
ICA
Ijth Entry
independent component analysis
matrix
Matrix Decompositions
matrix factorisation techniques in research
NNMF
NNMF Algorithm
Nonnegative Matrix Factorizations
outer
Outer Product Matrix
Ramachandran Plot
scientific data exploration
SDD
space
Spatial Artifacts
spectral analysis
Statistically Independent
Superimposed
tensor
Tensor Decomposition
tensors
Truncated SVD
Tucker3 Decomposition
unsupervised learning
Vice Versa
Product details
- ISBN 9781584888321
- Weight: 650g
- Dimensions: 156 x 234mm
- Publication Date: 17 May 2007
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book helps you determine which matrix is appropriate for your dataset and what the results mean.
Explaining the effectiveness of matrices as data analysis tools, the book illustrates the ability of matrix decompositions to provide more powerful analyses and to produce cleaner data than more mainstream techniques. The author explores the deep connections between matrix decompositions and structures within graphs, relating the PageRank algorithm of Google's search engine to singular value decomposition. He also covers dimensionality reduction, collaborative filtering, clustering, and spectral analysis. With numerous figures and examples, the book shows how matrix decompositions can be used to find documents on the Internet, look for deeply buried mineral deposits without drilling, explore the structure of proteins, detect suspicious emails or cell phone calls, and more.
Concentrating on data mining mechanics and applications, this resource helps you model large, complex datasets and investigate connections between standard data mining techniques and matrix decompositions.
Queen's University, Kingston, Ontario, Canada
