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Results 21 - 30
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4,362
Learning Bayesian network structure from massive datasets: the “sparse candidate” algorithm
- In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI
, 1999
"... Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a sta-tistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the sear ..."
Abstract
-
Cited by 247 (7 self)
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Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a sta-tistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since
Estimating Causal Effects from Large Data Sets Using Propensity
- Scores,”Annals of Internal Medicine
, 1997
"... The aim of many analyses of large databases is to draw causal inferences about the effects of actions, treatments, or interventions. Examples include the effects of various options available to a physician for treating a particular patient, the relative efficacies of various health care providers, ..."
Abstract
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Cited by 177 (5 self)
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, and the consequences of implementing a new national health care policy. A complication of using large databases to achieve such aims is that their data are almost always observational rather than experimental. That is, the data in most large data sets are not based on the results of carefully conducted randomized
Fuzzification and Reduction of Information-Theoretic Rule Sets
"... If-then rules are one of the most common forms of knowledge discovered by data mining methods. The number and the length of extracted rules tend to increase with the size of a database, making the rulesets less interpretable and useful. Existing methods of extracting fuzzy rules from numerical data ..."
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with constructing an information-theoretic network from a data table and extracting a set of association rules based on the network connections. The set of informationtheoretic rules is fuzzified and significantly reduced by using the principles of the Computational Theory of Perception (CTP). We demonstrate
Scalable Parallel Data Mining for Association Rules
, 1997
"... One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of ..."
Abstract
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Cited by 181 (12 self)
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One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset
Efficient Algorithms for Discovering Association Rules
, 1994
"... Association rules are statements of the form "for 90 % of the rows of the relation, if the row has value 1 in the columns in set W , then it has 1 also in column B". Agrawal, Imielinski, and Swami introduced the problem of mining association rules from large collections of data, and gave a ..."
Abstract
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Cited by 237 (11 self)
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Association rules are statements of the form "for 90 % of the rows of the relation, if the row has value 1 in the columns in set W , then it has 1 also in column B". Agrawal, Imielinski, and Swami introduced the problem of mining association rules from large collections of data, and gave
Mining Data Streams: A Review.
- SIGMOD Record,
, 2005
"... Abstract The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traff ..."
Abstract
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Cited by 113 (6 self)
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to address the problem of very large databases. Data mining is that interdisciplinary field of study that can extract models and patterns from large amounts of information stored in data repositories Recently, the data generation rates in some data sources become faster than ever before. This rapid
Double Competition for Information-Theoretic SOM
"... Abstract—In this paper, we propose a new type of information-theoretic method for the self-organizing maps (SOM), taking into account competition between competitive (output) neurons as well as input neurons. The method is called ”double competition”, as it considers competition between outputs as w ..."
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as well as input neurons. By increasing information in input neurons, we expect to obtain more detailed information on input patterns through the information-theoretic method. We applied the information-theoretic methods to two well-known data sets from the machine learning database, namely, the glass
An Information-Theoretic Framework towards Large-Scale Video
- Columbia University
, 2007
"... Video and image retrieval has been an active and challenging research area due to the explosive growth of online video data, personal video recordings, digital photos, and broadcast news videos. In order to effectively manage and use such enormous multimedia resources, users need to be able to acces ..."
Abstract
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Cited by 3 (0 self)
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. To fully exploit the potential of integrating multimodal features and ensure generality of solutions, this thesis presents a novel, rigorous framework and new statistical methods for video structuring, threading, and search in large-scale video databases. We focus on investigation of several fundamental
MineSet: An Integrated System for Data Mining
, 1997
"... MineSet TM , Silicon Graphics' interactive system for data mining, integrates three powerful technologies: database access, analytical data mining, and data visualization. It supports the knowledge discovery process from data access and preparation through iterative analysis and visualization t ..."
Abstract
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Cited by 67 (9 self)
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MineSet TM , Silicon Graphics' interactive system for data mining, integrates three powerful technologies: database access, analytical data mining, and data visualization. It supports the knowledge discovery process from data access and preparation through iterative analysis and visualization
General Constructions for Information-Theoretic Private Information Retrieval
, 2003
"... A Private Information Retrieval (PIR) protocol enables a user to retrieve a data item from a database while hiding the identity of the item being retrieved; specifically, in a t-private, k-server PIR protocol the database is replicated among k servers, and the user's privacy is protected from a ..."
Abstract
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Cited by 23 (0 self)
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any collusion of up to t servers. The main cost-measure of such protocols is the communication complexity of retrieving asingle bit of data. This work addresses the information-theoretic setting for PIR, where the user's privacy should be un-conditionally protected against computationally
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