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  1. Data Mining and Knowledge Discovery
  2. Data Mining and Knowledge Discovery : Volume 27
  3. Data Mining and Knowledge Discovery : Volume 27, Issue 3, November 2013
  4. Growing a list
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Data Mining and Knowledge Discovery : Volume 31
Data Mining and Knowledge Discovery : Volume 30
Data Mining and Knowledge Discovery : Volume 29
Data Mining and Knowledge Discovery : Volume 28
Data Mining and Knowledge Discovery : Volume 27
Data Mining and Knowledge Discovery : Volume 27, Issue 3, November 2013
Guest editor’s introduction: special issue of the ECML PKDD 2013 journal track
ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS
Activity preserving graph simplification
A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies
Growing a list
What distinguish one from its peers in social networks?
Fast sequence segmentation using log-linear models
Guest editors’ introduction: special section of selected papers from ECML-PKDD 2012
An efficiently computable subgraph pattern support measure: counting independent observations
Nearly exact mining of frequent trees in large networks
Data Mining and Knowledge Discovery : Volume 27, Issue 2, September 2013
Data Mining and Knowledge Discovery : Volume 27, Issue 1, July 2013
Data Mining and Knowledge Discovery : Volume 26
Data Mining and Knowledge Discovery : Volume 25
Data Mining and Knowledge Discovery : Volume 24
Data Mining and Knowledge Discovery : Volume 23
Data Mining and Knowledge Discovery : Volume 22
Data Mining and Knowledge Discovery : Volume 21
Data Mining and Knowledge Discovery : Volume 20
Data Mining and Knowledge Discovery : Volume 19
Data Mining and Knowledge Discovery : Volume 18
Data Mining and Knowledge Discovery : Volume 17
Data Mining and Knowledge Discovery : Volume 16
Data Mining and Knowledge Discovery : Volume 15
Data Mining and Knowledge Discovery : Volume 14
Data Mining and Knowledge Discovery : Volume 13
Data Mining and Knowledge Discovery : Volume 12
Data Mining and Knowledge Discovery : Volume 11
Data Mining and Knowledge Discovery : Volume 10
Data Mining and Knowledge Discovery : Volume 9
Data Mining and Knowledge Discovery : Volume 8
Data Mining and Knowledge Discovery : Volume 7
Data Mining and Knowledge Discovery : Volume 6
Data Mining and Knowledge Discovery : Volume 5
Data Mining and Knowledge Discovery : Volume 4
Data Mining and Knowledge Discovery : Volume 3
Data Mining and Knowledge Discovery : Volume 2
Data Mining and Knowledge Discovery : Volume 1

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Growing a list

Content Provider SpringerLink
Author Letham, Benjamin Rudin, Cynthia Heller, Katherine A.
Copyright Year 2013
Abstract It is easy to find expert knowledge on the Internet on almost any topic, but obtaining a complete overview of a given topic is not always easy: information can be scattered across many sources and must be aggregated to be useful. We introduce a method for intelligently growing a list of relevant items, starting from a small seed of examples. Our algorithm takes advantage of the wisdom of the crowd, in the sense that there are many experts who post lists of things on the Internet. We use a collection of simple machine learning components to find these experts and aggregate their lists to produce a single complete and meaningful list. We use experiments with gold standards and open-ended experiments without gold standards to show that our method significantly outperforms the state of the art. Our method uses the ranking algorithm Bayesian Sets even when its underlying independence assumption is violated, and we provide a theoretical generalization bound to motivate its use.
Starting Page 372
Ending Page 395
Page Count 24
File Format PDF
ISSN 13845810
Journal Data Mining and Knowledge Discovery
Volume Number 27
Issue Number 3
e-ISSN 1573756X
Language English
Publisher Springer US
Publisher Date 2013-07-16
Publisher Place Boston
Access Restriction Subscribed
Subject Keyword Set completion Ranking Internet data mining Collective intelligence Data Mining and Knowledge Discovery Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Content Type Text
Resource Type Article
Subject Information Systems Computer Science Applications Computer Networks and Communications
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