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106,262
Maximum likelihood from incomplete data via the EM algorithm
- JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract
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Cited by 11972 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
The predictability of data values
- IN PROCEEDINGS OF THE 30TH INTERNATIONAL SYMPOSIUM ON MICROARCHITECTURE
, 1997
"... ..."
Privacy-Preserving Data Mining
, 2000
"... A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models with ..."
Abstract
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Cited by 844 (3 self)
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without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from tredning data in which the values of individual records have been perturbed. The resulting data records look very different from the original records
Mining the Network Value of Customers
- In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining
, 2002
"... One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected pro t from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only ..."
Abstract
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Cited by 568 (11 self)
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One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected pro t from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered
Data Security
, 1979
"... The rising abuse of computers and increasing threat to personal privacy through data banks have stimulated much interest m the techmcal safeguards for data. There are four kinds of safeguards, each related to but distract from the others. Access controls regulate which users may enter the system and ..."
Abstract
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Cited by 615 (3 self)
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and subsequently whmh data sets an active user may read or wrote. Flow controls regulate the dissemination of values among the data sets accessible to a user. Inference controls protect statistical databases by preventing questioners from deducing confidential information by posing carefully designed sequences
Implementing data cubes efficiently
- In SIGMOD
, 1996
"... Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total ..."
Abstract
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Cited by 548 (1 self)
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total sales. The values of many of these cells are dependent on the values of other cells in the data cube..A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach
Bayesian Data Analysis
, 1995
"... I actually own a copy of Harold Jeffreys’s Theory of Probability but have only read small bits of it, most recently over a decade ago to confirm that, indeed, Jeffreys was not too proud to use a classical chi-squared p-value when he wanted to check the misfit of a model to data (Gelman, Meng and Ste ..."
Abstract
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Cited by 2194 (63 self)
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I actually own a copy of Harold Jeffreys’s Theory of Probability but have only read small bits of it, most recently over a decade ago to confirm that, indeed, Jeffreys was not too proud to use a classical chi-squared p-value when he wanted to check the misfit of a model to data (Gelman, Meng
Value-Based Software Engineering
- ACM Software Engineering Notes
, 2003
"... Abstract—This paper provides a definition of the term “software engineering ” and a survey of the current state of the art and likely future trends in the field. The survey covers the technology available in the various phases of the software life cycle—requirements engineering, design, coding, test ..."
Abstract
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Cited by 472 (32 self)
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set of 104 references is provided. Index Terms—Computer software, data systems, information systems,
On understanding types, data abstraction, and polymorphism
- ACM COMPUTING SURVEYS
, 1985
"... Our objective is to understand the notion of type in programming languages, present a model of typed, polymorphic programming languages that reflects recent research in type theory, and examine the relevance of recent research to the design of practical programming languages. Object-oriented languag ..."
Abstract
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Cited by 845 (13 self)
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-oriented languages provide both a framework and a motivation for exploring the interaction among the concepts of type, data abstraction, and polymorphism, since they extend the notion of type to data abstraction and since type inheritance is an important form of polymorphism. We develop a λ-calculus-based model
Missing value estimation methods for DNA microarrays
, 2001
"... Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clu ..."
Abstract
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Cited by 477 (24 self)
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Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K
Results 1 - 10
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