Revision as of 15:04, 19 November 2015

#!PATTERN+

info

Pattern+ is an annotated edition of the webmining module Pattern. The alpha-release of Pattern+ serves two interconnected goals: It is first of all an attempt to grow our understanding of - and concern with - the culture of data-mining, and then to feed this back into the practice of data-mining itself. It is also an experiment with forms of collaborative techno-critique that locate themselves closely to actual software objects. (from: #!PATTERN+.readme)

Contributors are Manetta Berends, Christina Colchoir, Frederic Janssens, Anne Laforet, My Lê, Femke Snelting, Kym Ward.

Relearn

Pattern+ is initiated as a project during the Relearn summerschool 2015 in Brussels.

material from Relearn 2015

* Knowledge Discovery in Data (KDD) steps
* data mining culture

other resources

* earlier notes (Jul. 2015)
* notes taken during Relearn Aug. 2015, training common sense
* #!PATTERN+.readme

* the Annotator, Cqrrelations Jan. 2015, on etherpad
* notes Cqrrelations day 1, day 2, day 3, day 4, day 5

notes

* problems related to the common-sense results:
- "it is only possible to categorize, after you have defined the categories" — Solon Barocas at Cqrrelations, 2015
- how can we escape the vicious circle (of training by showing examples of current 'status quo')?
- when an algorithm is made to predict a certain 'truth', how does it find the unpredictable / margins?

@@ Line 1: / Line 1: @@
-<div style="width:75%;">
+<div style="width:100%;max-width:850px;">
 __NOTOC__
 =#!PATTERN+=
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   * problems related to the common-sense results:
-  - "it is only possible to categorise, after you have defined the categories" — Solon Barocas at Cqrrelations, 2015
+  - "it is only possible to categorize, after you have defined the categories" <small>— Solon Barocas at Cqrrelations, 2015</small>
-  - how can we escape the vicious circle  (of training by showing examples of current 'status quo')?
+  - how can we escape the vicious circle (of training by showing examples of current 'status quo')?
   - when an algorithm is made to predict a certain 'truth', how does it find the unpredictable / margins?
 ==related==