Big data landscapes - improving the visualization of machine learning-based clustering algorithms.

Dietrich Kammer, Mandy Keck, Thomas Gründer, Rainer Groh

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

With the internet, massively heterogeneous data sources need to be understood and classified to provide suitable services to users such as content observation, data exploration, e-commerce, or adaptive learning environments. The key to providing these services is applying machine learning (ML) in order to generate structures via clustering and classification. Due to the intricate processes involved in ML, visual tools are needed to support designing and evaluating the ML pipelines. In this contribution, we propose a comprehensive tool that facilitates the analysis and design of ML-based clustering algorithms using multiple visualization features such as semantic zoom, glyphs, and histograms.

Original languageEnglish
Pages66:1-66:3
DOIs
Publication statusPublished - 29 May 2018
Externally publishedYes
EventAVI 2018 – International Working Conference on Advanced Visual Interfaces - Grosseto, Italy
Duration: 29 May 20181 Jun 2018

Conference

ConferenceAVI 2018 – International Working Conference on Advanced Visual Interfaces
CountryItaly
CityGrosseto
Period29.05.201801.06.2018

Keywords

  • Big Data Landscapes
  • Clustering
  • Glyphs
  • Machine Learning
  • Visualization
  • information visualization

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