Lukasz Stafiniak pages | DataMining / Data Mining

May 21, 2009, at 02:54 PM by lukstafi - big data

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* [[http://radar.oreilly.com/2009/05/big-data-analytics-r-linked-data-ssd.html | Big Data: SSD's, R, and Linked Data Streams]]

February 06, 2009, at 12:56 AM by lukstafi - DM blog

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Featured links:
* [[http://dataminingwarehousing.blogspot.com/ | Data Mining and Warehousing blog]]

September 19, 2008, at 02:34 PM by lukstafi -

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* [[http://www.liaad.up.pt/~ltorgo/DataMiningWithR/ | Data Mining with R: learning by case studies]] (by Luis Torgo), ~~seems excellent!~~

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* [[http://www.liaad.up.pt/~ltorgo/DataMiningWithR/ | Data Mining with R: learning by case studies]] (by Luis Torgo), nice tutorial with SQL interaction

September 19, 2008, at 02:21 PM by lukstafi - link to a great tutorial

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Other links for the class:
* [[http://www.liaad.up.pt/~ltorgo/DataMiningWithR/ | Data Mining with R: learning by case studies]] (by Luis Torgo), seems excellent!
* [[http://zoonek2.free.fr/UNIX/48_R/all.html | Statistics with R]], a very interesting but unpolished course on R
* [[http://cc.oulu.fi/~jarioksa/opetus/metodi/eda.pdf | Introduction to R and Exploratory data analysis]]
* [[http://www.math.csi.cuny.edu/Statistics/R/simpleR/index.html | Using R for Introductory Statistics]] (pre-draft of a published book)
* [[http://cran.r-project.org/doc/contrib/usingR.pdf | Using R for data analysis and graphics]] (JH Maindonald)
** from [[http://www.biostat.wisc.edu/~kbroman/Rintro/ | Introduction to R]] (a collection of links by Karl W Broman)
* [[http://www.cs.iastate.edu/~honavar/Papers/caragea-thesis.pdf | Learning classifiers from distributed, semantically heterogeneous, autonomous data sources]]

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Other links for the class:
* [[http://www.cs.iastate.edu/~honavar/Papers/caragea-thesis.pdf | Learning classifiers from distributed, semantically heterogeneous, autonomous data sources]]
* [[http://zoonek2.free.fr/UNIX/48_R/all.html | Statistics with R]], a very interesting course on R
* [[(Wikipedia:)Exploratory data analysis]]
* [[http://octave.sourceforge.net/doc/funref_statistics.html | Extra statistical functions for Octave]], among them @@boxplot@@.
* [[http://cc.oulu.fi/~jarioksa/opetus/metodi/eda.pdf | Introduction to R and Exploratory data analysis]]
* [[http://www.math.csi.cuny.edu/Statistics/R/simpleR/index.html | Using R for Introductory Statistics]] (pre-draft of a published book)
* [[http://cran.r-project.org/doc/contrib/usingR.pdf | Using R for data analysis and graphics]] (JH Maindonald)
** from [[http://www.biostat.wisc.edu/~kbroman/Rintro/ | Introduction to R]] (a collection of links by Karl W Broman)

July 18, 2008, at 11:28 AM by lukstafi -

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* [[Projekt, dr Leszek Grocholski -> Attach:projektHD.doc]]

June 20, 2008, at 09:45 AM by lukstafi - time series link

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** [[http://www.scausa.com/DataMiningOnTimeSeries2.pdf | Data mining on time series: an illustration using fast-food restaurant franchise data]]

June 20, 2008, at 03:48 AM by lukstafi - RDFowe bazy danych

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** [[http://mondrian.pentaho.org/ | Mondrian]] is an OLAP server written in Java. It enables you to interactively analyze very large datasets stored in SQL databases without writing SQL.

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** [[http://www.ltg.ed.ac.uk/np/publications/ltg/papers/Byrne2006Tethering.pdf | Tethering Cultural Data with RDF]] (Kate Byrne)
*** [[http://www.opencog.org/wiki/RelEx | RelEx]] is an English-language semantic relationship extractor, built on the Carnegie-Mellon link parser
** Bazy danych lub "interfejsy" do baz danych:
*** [[http://openrdf.org/ | Sesame]] is an open source framework for storage, inferencing and querying of RDF data.
*** [[http://mondrian.pentaho.org/ | Mondrian]] is an OLAP server written in Java. It enables you to interactively analyze very large datasets stored in SQL databases without writing SQL.
*** [[the Exist XML database -> http://exist.sourceforge.net/]]
*** [[the RDF/SPARQL/XML part of the OpenLink Virtuoso system -> http://sourceforge.net/projects/virtuoso/]]
*** [[the Jena/ARQ combo -> http://jena.sourceforge.net/ARQ/]]
*** [[http://www.kobrix.com/hgdb.jsp | HyperGraphDB]]
*** [[http://www.intellidimension.com/ | RDF Gateway]] (komercyjne)
*** [[http://4suite.org/index.xhtml | 4Suite: an open-source platform for XML and RDF processing]]
*** [[http://rx4rdf.liminalzone.org/ | Rx4RDF]]

June 20, 2008, at 02:51 AM by lukstafi - RDF and graph databases

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** [[http://www.dcc.uchile.cl/~cgutierr/papers/eswc05.pdf | Querying RDF Data from a Graph Database Perspective]] (Renzo Angles and Claudio Gutierrez), [[http://www.ciw.cl/material/irw-2005/2005-irw-gutierrez.pdf | Querying from a Graph Database Perspective: the case of RDF]] -- prezentacja

June 06, 2008, at 07:57 AM by lukstafi - projection pursuit PCA

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** [[http://zoonek2.free.fr/UNIX/48_R/05.html | Statistics with R: Factorial methods: Around Principal Component Analysis (PCA)]]
** [[http://www.r-project.org/useR-2006/Slides/Fritz.pdf | PCA by Projection Pursuit. The Package pcaPP]] Heinrich Fritz
** [[http://cran.r-project.org/doc/Rnews/Rnews_2003-3.pdf | Dimensional Reduction for Data Mapping]] (Jonathan Edwards and Paul Oman) R News, 3, 2003

May 30, 2008, at 04:50 PM by lukstafi -

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** [[http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf | A Practical Guide to Support Vector ~~Classiﬁcation~~]]

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** [[http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf | A Practical Guide to Support Vector Classification]]

May 30, 2008, at 04:19 PM by lukstafi - SVM guide

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** [[http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf | A Practical Guide to Support Vector Classiﬁcation]]

May 23, 2008, at 03:07 AM by lukstafi - rpart

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** [[http://www.mayo.edu/hsr/techrpt/61.pdf | An Introduction to Recursive Partitioning Using the RPART Routines]] ([[http://ndc.mayo.edu/mayo/research/biostat/upload/rpartmini.pdf | wersja skrócona]] -- bez opisu teoretycznego)

May 21, 2008, at 06:45 PM by lukstafi - PCA zadanie

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* [[Zadanie7]] (szeregi czasowe)

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* [[Zadanie7]] (wizualizacja danych)
* [[Zadanie8]] (szeregi czasowe)

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* [[~~Zadanie8~~]] ("niestandardowe" bazy danych)

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* [[Zadanie9]] ("niestandardowe" bazy danych)

May 21, 2008, at 06:31 PM by lukstafi - plan zadan

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* [[Zadanie1]]
* [[Zadanie2]]
* [[Zadanie3]]

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* [[Zadanie1]] (eksploracyjna analiza danych)
* [[Zadanie2]] (rozkład normalny i symulacja/generowanie)
* [[Zadanie3]] (wielojądrowe SVMy)

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* [[Zadanie4]]

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* [[Zadanie4]] (sieci Bayesowskie i reguły asocjacyjne)

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* [[Zadanie5]]

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* [[Zadanie5]] (klasyfikacja, drzewa decyzyjne)

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* [[Zadanie6]] (grupowanie, metody hierarchiczne, ocena ilości skupień)
** [[http://zoonek2.free.fr/UNIX/48_R/06.html | Statistics with R: Clustering]]
* [[Zadanie7]] (szeregi czasowe)
** [[http://zoonek2.free.fr/UNIX/48_R/15.html | Statistics with R: Time series]]
* [[Zadanie8]] ("niestandardowe" bazy danych)
** [[http://mondrian.pentaho.org/ | Mondrian]] is an OLAP server written in Java. It enables you to interactively analyze very large datasets stored in SQL databases without writing SQL.

May 16, 2008, at 07:38 AM by lukstafi - zadanie 5 -- klasyfikacja

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* [[Zadanie5]]
** [[http://zoonek2.free.fr/UNIX/48_R/12.html | Statistics with R: Rozdział 12]] Using regression in a classification problem, Nearest Neighbours, Naive Bayes classifier
** [[http://cran.r-project.org/doc/Rnews/Rnews_2002-3.pdf | Classification and regression by randomForest]] R News, 3, 2002
** [[http://stats.math.uni-augsburg.de/Klimt/features.html | KLIMT Making trees interactive]] (Klassification - Interactive Methods for Trees)

April 25, 2008, at 12:35 PM by lukstafi - \

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# Tematy z książki "The Handbook of Data Mining"

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# Tematy z książki "The Handbook of Data Mining". Można wybrać jeden rozdział lub kilka powiązanych rozdziałów. [wszystkie wolne]

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23 Mining Science and Engineering Data 549 (Chandrika Kamath)

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## 23 Mining Science and Engineering Data 549 (Chandrika Kamath)

April 25, 2008, at 12:32 PM by lukstafi - referaty handbook

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**

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# Tematy z książki "The Handbook of Data Mining"
## 13 Distributed Data Mining 341 (Byung-Hoon Park and Hillol Kargupta)
## II: MANAGEMENT OF DATA MINING 14 Data Collection, Preparation, Quality, and Visualization 365 (Dorian Pyle)
## 15 Data Storage and Management 393 (Tong (Teresa) Wu and Xiangyang (Sean) Li)
## 16 Feature Extraction, Selection, and Construction 409 (Huan Liu, Lei Yu, and Hiroshi Motoda)
## 17 Performance Analysis and Evaluation 425 (Sholom M. Weiss and Tong Zhang)
## 18 Security and Privacy 441 (Chris Clifton)
## 19 Emerging Standards and Interfaces 453 (Robert Grossman, Mark Hornick, and Gregor Meyer)
## III: APPLICATIONS OF DATA MINING 20 Mining Human Performance Data 463 (David A. Nembhard)
## 21 Mining Text Data 481 (Ronen Feldman)
## 22 Mining Geospatial Data 519 (Shashi Shekhar and Ranga Raju Vatsavai)
23 Mining Science and Engineering Data 549 (Chandrika Kamath)
## 24 Mining Data in Bioinformatics 573 (Mohammed J. Zaki)
## 25 Mining Customer Relationship Management (CRM) Data 597 (Robert Cooley)
## 26 Mining Computer and Network Security Data 617 (Nong Ye)
## 27 Mining Image Data 637 (Chabane Djeraba and Gregory Fernandez)
## 28 Mining Manufacturing Quality Data 657 (Murat C. Testik and George C. Runger)

April 25, 2008, at 07:07 AM by lukstafi - BNT, zadanie4

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* [[Zadanie4]]
** [[http://www.autonlab.org/tutorials/shortbayes.html | Short Overview of Bayes Nets]], [[Inference in Bayesian Networks -> http://www.autonlab.org/tutorials/bayesinf.html]] [[http://www.autonlab.org/tutorials/bayesstruct.html | Learning Bayesian Networks]]: Tutorial Slides by Andrew Moore
** [[http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html | Bayes Net Toolbox for Matlab]] Written by Kevin Murphy, 1997--2002: [[http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html | A Brief Introduction to Graphical Models and Bayesian Networks]], [[http://www.cs.ubc.ca/~murphyk/Software/BNT/usage.html | How to use the toolbox]]

**

April 11, 2008, at 10:27 PM by lukstafi - referaty początek

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Propozycje referatów (w trakcie opracowywania):
# Komunikacja z bazą danych z poziomu środowiska R. [zajęty]
# Automatyczne generowanie raportów w środowisku R. [wolny]
# Tematy z [[http://www.cs.iastate.edu/~honavar/Papers/caragea-thesis.pdf | Learning classifiers from distributed, semantically heterogeneous, autonomous data sources]]: [wszystkie wolne]
## Rozdział trzeci: "Learning classifiers from distributed data",
## Rozdział czwarty: "Learning classifiers from sementically heterogeneous data",
## W razie zainteresowania możemy zrobić więcej.

April 11, 2008, at 09:39 PM by lukstafi - learning from distributed sources

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* [[http://www.cs.iastate.edu/~honavar/Papers/caragea-thesis.pdf | Learning classifiers from distributed, semantically heterogeneous, autonomous data sources]]

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WARNING: Old stuff below.

April 11, 2008, at 04:30 AM by lukstafi - SVMs, learning with heterogeneous data

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** [[http://www.autonlab.org/tutorials/svm15.pdf | Support Vector Machines, Tutorial Slides by Andrew Moore]], [[http://www.support-vector.net/icml-tutorial.pdf | Support Vector and Kernel Machines]]
** [[http://video.google.pl/videoplay?docid=4867582015325197740 | Sparse and large-scale learning with heterogeneous data]]
** [[http://www.potschi.de/svmtut/svmtut.html | SVM-Tutorial using R (e1071-package)]]
** [[http://www.shogun-toolbox.org/ | Shogun - A Large Scale Machine Learning Toolbox]]

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* [[http://zoonek2.free.fr/UNIX/48_R/all.html | Statistics with R]], a very interesting course on R

April 04, 2008, at 08:04 AM by lukstafi - HD projekt

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* [[Projekt, dr Leszek Grocholski -> Attach:projektHD.doc]]

April 04, 2008, at 02:35 AM by lukstafi - zad3 real soon now

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* [[Zadanie3]]

March 28, 2008, at 01:21 AM by lukstafi - zadanie 2

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~~Links~~ for the class:

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Linki na pracownię:
* [[Attach:zadania.pdf]]
* [[Attach:introOctaveR.pdf | Wprowadzenie do języków Octave i R]]
* [[Zadanie1]]
* [[Zadanie2]]

Other links for the class:

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Linki na pracownię:
* [[Attach:zadania.pdf]]
* [[Attach:introOctaveR.pdf | Wprowadzenie do języków Octave i R]]
* [[Zadanie1]]

March 20, 2008, at 06:00 AM by lukstafi - pracownia linki

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~~[[Attach~~:zadania.pdf]]

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Linki na pracownię:
* [[Attach:zadania.pdf]]
* [[Attach:introOctaveR.pdf | Wprowadzenie do języków Octave i R]]
* [[Zadanie1]]

March 19, 2008, at 11:39 PM by lukstafi - R EDA link

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* [[http://cc.oulu.fi/~jarioksa/opetus/metodi/eda.pdf | Introduction to R and Exploratory data analysis]]

March 19, 2008, at 02:03 AM by lukstafi - R links

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* [[http://www.math.csi.cuny.edu/Statistics/R/simpleR/index.html | Using R for Introductory Statistics]] (pre-draft of a published book)
* [[http://cran.r-project.org/doc/contrib/usingR.pdf | Using R for data analysis and graphics]] (JH Maindonald)
** from [[http://www.biostat.wisc.edu/~kbroman/Rintro/ | Introduction to R]] (a collection of links by Karl W Broman)

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* [[http://www.wired.com/techbiz/media/magazine/16-03/mf_netflix?currentPage=1 | Wired: This Psychologist Might Outsmart the Math Brains Competing for the Netflix Prize]]

March 14, 2008, at 04:33 AM by lukstafi - extra stat for Octave

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* [[http://octave.sourceforge.net/doc/funref_statistics.html | Extra statistical functions for Octave]], among them @@boxplot@@.

March 14, 2008, at 04:27 AM by lukstafi - exploratory data analysis

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Links for the class:
* [[(Wikipedia:)Exploratory data analysis]]

March 13, 2008, at 11:30 PM by lukstafi -

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[[Attach:zadania.pdf]]

January 26, 2008, at 08:56 PM by lukstafi - places

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Places:
* [[http://research.google.com/ | Google Research]]

January 25, 2008, at 07:41 PM by lukstafi - google datastore

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* [[Wikipedia:Petabyte#Petabytes_in_use]]

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* [[Wikipedia:Petabyte#Petabytes_in_use]]
* [[http://arstechnica.com/news.ars/post/20080122-rumors-suggest-google-is-set-to-open-scientific-data-store.html | Arstechnica: Rumors suggest Google is set to open scientific data store]]

January 24, 2008, at 10:58 PM by lukstafi - petabytes

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Amusements:
* [[Wikipedia:Petabyte#Petabytes_in_use]]

December 27, 2007, at 09:36 PM by lukstafi - learning library

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Software:
* [[http://hunch.net/~vw/ | Vowpal Wabbit (Fast Online Learning)]]

December 21, 2007, at 11:15 PM by lukstafi - FilterBoost

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* [[http://video.google.com/videoplay?docid=4867582015325197740 | Sparse and large-scale learning with heterogeneous data]], Google Tech ~~Talk~~

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* [[http://video.google.com/videoplay?docid=4867582015325197740 | Sparse and large-scale learning with heterogeneous data]], Google Tech Talk
* [[http://www.cs.cmu.edu/~jkbradle/ | FilterBoost: Regression and Classification on Large Datasets]], Joseph K. Bradley and Robert E. Schapire

October 03, 2007, at 01:39 AM by lukstafi - Sparse and large-scale learning with heterogeneous data

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* [[http://nars.wang.googlepages.com/wang.preference.pdf | Recommendation Based on Personal Preference]], Pei Wang. (Asking a database questions like: "give me five best examples of fast and cheap notebook", "find cheap ticket for a flight that leaves C around 9AM and arives at D as early as possible".)

to:

* [[http://nars.wang.googlepages.com/wang.preference.pdf | Recommendation Based on Personal Preference]], Pei Wang. (Asking a database questions like: "give me five best examples of fast and cheap notebook", "find cheap ticket for a flight that leaves C around 9AM and arives at D as early as possible".)
* [[http://video.google.com/videoplay?docid=4867582015325197740 | Sparse and large-scale learning with heterogeneous data]], Google Tech Talk

September 15, 2007, at 01:39 PM by lukstafi - Pei Wang preferences

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* [[http://nars.wang.googlepages.com/wang.preference.pdf | Recommendation Based on Personal Preference]], Pei Wang. (Asking a database questions like: "give me five best examples of fast and cheap notebook", "find cheap ticket for a flight that leaves C around 9AM and arives at D as early as possible".)

September 08, 2007, at 10:20 PM by lukstafi - IR, picture analysis

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Some random links for now:

* [[http://www.ii.uni.wroc.pl/%7Etju/Wyszukiwanie07/wyszukiwanie07.html | Information Retrieval (Searching the Web)]], Tomasz Jurdzinski course and links from there
* [[http://portal.acm.org/citation.cfm?id=1133508 | Picture languages in machine understanding of medical visualization]], Marek R. Ogiela, Ryszard Tadeusiewicz
* [[http://library.epfl.ch/theses/?nr=3729 | Learning the structure of image collections with latent aspect models]], Florent Monay

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