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PAZUR 32

Event:
PAZUR 32
Event type:
Meetup
Category:
IT
Topic:
Date:
23.05.2019 (thursday)
Time:
18:00
Language:
Polish
Price:
Free
City:
Place:
Uniwersytet Ekonomiczny w Poznaniu
Address:
al. Niepodległości 10
Description:

Przewidujemy dwie prezentacje w języku angielskim.


1. Segmentation using NMF decomposition -- Marcin Kosiński


Spotkanie odbędzie się w sali 003A (przyziemie, Gmach Główny UEP, Al. Niepodległości 10).


Spotkanie odbywa się w ramach cyku spotkań poprzedzających konferencję WhyR.


This pre-meeting promots

  • Why R? 2019 Conference http://whyr.pl/2019/
  • EARLY BIRD Registration http://whyr.pl/2019/register/
  • Call for Papers http://whyr.pl/2019/submit/


# Segmentation using NMF decomposition -- Marcin Kosiński


From the nowadays segmentation, we require them to follow below features:

  • it should be balanced,
  • segments should be distinctive,
  • the discovered over and under indexed features within segments should create a meaningful story,
  • and in the best case the amount of differentiative factors that drives segmentation should be small.


The last requirement often is a bottleneck in the scenario of a survey where respondents are asked enormous amount of questions.


The solution, one from many, to this use case can be the nonnegative matrix factorization that in a one attempt segments respondents and their features!


I'll present concept of the NMF decomposition and I'll present applications in R, with the explanation of diagnostic plots.


Working with high dimensional data? Often facing the need to group observations? That's a good presentation for you.


# Speakers bio


Marcin Kosiński has a master degree in Mathematical Statistics and Data Analysis specialty. Challenges seeker and devoted R language enthusiast. In the past, keen on the field of large-scale online learning and various approaches to personalized news article recommendation.

Community events host: organizer of Why R? conferences.

Interested in R packages development and survival analysis models. Currently explores and improves methods for quantitative marketing analyses and global surveys at Gradient Metrics.

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