Posts Tagged ‘mining’

Twitter “Exhaust” Reveals Patterns of Unemployment | MIT Technology Review

December 1, 2014

Social media fingerprints of unemployment, from detecting network components in tweet mining arxiv.org/abs/1411.3140 +
http://www.technologyreview.com/view/532746/twitter-exhaust-reveals-patterns-of-unemployment

Lots of press for an arxiv paper, viz:
Twitter “Exhaust” Reveals Patterns of Unemployment | MIT Technology Review

QT:{{”

So the team analysed the rate at which messages were exchanged between regions using a standard community detection algorithm. This revealed 340 independent areas of economic activity, which largely coincide with other measures of geographic and economic distribution. “This result shows that the mobility detected from geolocated tweets and the communities obtained are a good description of economical areas,” they say.

Finally, they looked at the unemployment figures in each of these regions and then mined their database for correlations with twitter activity.

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The Dark Market for Personal Data – NYTimes.com

October 26, 2014

The Dark Market for Personal Data
http://www.nytimes.com/2014/10/17/opinion/the-dark-market-for-personal-data.html We’re all “judged by a #bigdata Star Chamber of unaccountable decision makers”

QT:{{”

We need regulation to help consumers recognize the perils of the new information landscape without being overwhelmed with data. The right to be notified about the use of one’s data and the right to challenge and correct errors is fundamental. Without these protections, we’ll continue to be judged by a big-data Star Chamber of unaccountable decision makers using questionable sources.

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Delving into Deep Learning » American Scientist

October 16, 2014

Delving into Deep Learning http://www.americanscientist.org/issues/pub/2014/3/delving-into-deep-learning History of #NeuralNets from perceptrons to today’s complex nets with many hidden layers

My public notes from the Yale Day of Data (#ydod2014, i0dataday)

September 30, 2014

https://linkstream2.gerstein.info/tag/i0dataday
http://elischolar.library.yale.edu/dayofdata/2014/
https://storify.com/markgerstein/tweets-related-to-the-yale-day-of-data-2014-ydod20

PLOS Computational Biology: Spatial Generalization in Operant Learning: Lessons from Professional Basketball

September 29, 2014

Spatial Generalization in… Learning: Lessons from… #Basketball http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003623 How past success changes your tendencies to shoot

Describes constructing a learning matrix for how a player will update his tendency to shoot
from a certain region of the court based on his past successes or failures

Biostatistics: iCluster | Memorial Sloan Kettering Cancer Center

September 10, 2014

simultaneously clustering of cancer data across data types, in a sense related to orthoclust
http://www.mskcc.org/research/epidemiology-biostatistics/biostatistics/icluster

My public notes from KDD 2014

August 31, 2014

https://storify.com/markgerstein/tweets-related-to-kdd-2014-i0kdd-kdd2014

https://www.flickr.com/photos/mbgmbg/tags/seriesspacyworldofbloombergbldg

https://linkstream2.gerstein.info/tag/i0kdd/

http://archive.gersteinlab.org/meetings/s/2014/08.28/kdd2014-i0kdd-meeting-materials/ (need password)

http://www.kdd.org/kdd2014/

Big Data and Its Technical Challenges | July 2014 | Communications of the ACM

August 30, 2014

#BigData & Its Technical Challenges
http://cacm.acm.org/magazines/2014/7/176204-big-data-and-its-technical-challenges/abstract Data acquisition, cleaning, aggregation, analysis, visualization & interpretation

IEEE Xplore Abstract – A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

August 24, 2014

Pandey mentions: Comparative Analysis of #Ensemble Classifiers [eg mean agg. or stacking]…in Genomics
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6729565&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6729565 #kdd2014

performance-diversity tradeoff: should one incl. higher performance, lower diversity ones…. but still adding diversity is good

related to https://github.com/shwhalen/datasink

The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t: Nate Silver: 9781594204111: Amaz on.com: Books

July 8, 2014

http://www.amazon.com/The-Signal-Noise-Many-Predictions/dp/159420411X