Practice the three basic tasks of visual data analytics use data from mini project #1 (or other), begin with IN12500, ID1210) client-server system: python for processing (server), D3 for VIS (client)
Task 1: data clustering and decimation (30 points) implement random sampling and stratified sampling (remove 75% of data) the latter includes the need for k-means clustering (optimize k using elbow)
Task 2: dimension reduction on both org and 2 types of reduced data (30) find the intrinsic dimensionality of the data using PCA produce scree plot visualization and mark the intrinsic dimensionality show the scree plots before/after sampling to assess the bias introduced
- obtain the three attributes with highest PCA loadings
Task 3: visualization of both original and 2 types of reduced data (40 points) visualize the data projected into the top two PCA vectors via 2D scatterplot visualize the data via MDS (Euclidian & correlation distance) in 2D scatterplots visualize the scatterplot matrix of the three highest PCA loaded attributes
Due date: Tuesday, 3/10 Note: this is a time-intensive project — start early!!
You need to upload to Blackboard the following by the due date: 2-3 page report with illustrated description of your program’s capabifities and implementation detail add code snippets to show how you did things discuss interesting observations you made in the data constructively compare the various visualization alternatives compare the effects of down sampling (2 methods) with the original data make good use of visualizations video file that shows all features of your software in action archive file with source code
