Data Mining - Stanford University
A common sort of data-mining problem involves discovering unusual events hidden within massive amounts of data. This section is a discussion of the ... sense that they have no cause other than that random data will always have some number of unusual features that look significant but aren’t. A theorem
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Query Languages for XML - Stanford University
infolab.stanford.eduQuery Languages for XML XPath XQuery XSLT. 2 The XPath/XQueryData Model Corresponding to the fundamental “relation” of the relational model is: sequence of items. An item is either: 1. A primitive value, e.g., integer or string. 2. A node (defined next). 3 Principal Kinds of Nodes 1. Document nodes represent entire
DATABASES IN HEALTHCARE - Stanford University
infolab.stanford.eduDATABASES IN HEALTHCARE bY Gio Wiederhold Research sponsored by National Institutes of Health ... clinical trials, clinical research, ambulatory care, and hospitals) are appended. There is an extended bibliography.. ... file systems will simply disallow such access, in other systems such usage ...
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Computer Science: The Mechanization of Abstraction
infolab.stanford.edu4 COMPUTER SCIENCE: THE MECHANIZATION OF ABSTRACTION Fluffy Cat Animal Fluffy’s milk saucer is is owns Fig. 1.2. A graph representing knowledge about Fluffy. 2. Data structures, the programming-language constructs used to represent data
The Anatomy of a Search Engine - Stanford University
infolab.stanford.eduThe Anatomy of a Large-Scale Hypertextual Web Search Engine Sergey Brin and Lawrence Page Computer Science Department, Stanford University, Stanford, CA 94305, USA
The Relational Data Model - The Stanford University InfoLab
infolab.stanford.edu404 THE RELATIONAL DATA MODEL An important part of the design process is selecting “attributes,” or properties of the described objects, that can be kept together in a table, without introduc-
preface - The Stanford University InfoLab
infolab.stanford.eduPREFACE xi 4. Lists: all of Chapter 6. Some may wish to cover lists before trees, which is a more traditional treatment. We regard trees as the more fundamental
Mining of Massive Datasets - The Stanford University InfoLab
infolab.stanford.eduPreface This book evolved from material developed over several years by Anand Raja-raman and Jeff Ullman for a one-quarter course at Stanford.
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The Tree Data Model - The Stanford University InfoLab
infolab.stanford.edu226 THE TREE DATA MODEL If m1,m2,...,mk is a path in a tree, node m1 is called an ancestor of mk and node mk a descendant of m1.If the path is of length 1 or more, then m1 is called a Proper ancestor proper ancestor of mk and mk a proper descendant of m1.Again, remember that and descendant the case of a path of length 0 is possible, in which case the path lets us conclude
Recommendation Systems - Stanford University
infolab.stanford.eduChapter 9 Recommendation Systems There is an extensive class of Web applications that involve predicting user responses to options. Such a facility is called a recommendation system. We shall begin this chapter with a survey of the most important examples of these systems. However, to bring the problem into focus, two good examples of
Dimensionality Reduction - Stanford University
infolab.stanford.edunonzero vector x0 and then iterate: xk+1:= Mxk kMxkk where kNk for a matrix or vector N denotes the Frobenius norm; that is, the square root of the sum of the squares of the elements of N. We multiply the current vector xk by the matrix M until convergence (i.e., kxk − xk+1k is less than some small, chosen constant). Let x be xk for that ...
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