/Flight-data-Analytics

Mini project 2 of CSE 564 - Visualization class taken by Professor Klaus Muller. Implemented data clustering using random & stratified sampling, data decimation using MDS and PCA andinteractive data visualization using scatter plot matrix.

Primary LanguageJavaScript

Visualization-lab2--Flight-data-Analytics

Practice the three basic tasks of visual data analytics

  • use data from mini project #1 (or other), begin with |N|≥500, |D|≥10)
  • client-server system: python for processing (server), D3 for VIS (client)

Task1: data clustering and decimation (30 points)

  • implement random sampling and stratified sampling
  • the latter includes the need for k-means clustering (optimize k using elbow)

Task 2: dimension reduction (use decimated data) (30 points)

  • find the intrinsic dimensionality of the data using PCA
  • produce scree plot visualization and mark the intrinsic dimensionality
  • obtain the three attributes with highest PCA loadings

Task 3: visualization (use dimension reduced data) (40 points)

  • visualize data projected into the top two PCA vectors via 2D scatterplot
  • visualize data via MDS (Euclidian & correlation distance) in 2D scatterplots
  • visualize scatterplot matrix of the three highest PCA loaded attributes

Youtube link to view results : https://www.youtube.com/watch?v=EV9T5XxKSWc

Visualization Lab 3 - Volume Rendering

Also added the renderings with ImageVis software.

  • Renderings_with_imagevis_task_3 folder