Demonstates three basic tasks of visual data analytics uses data of forest fires (fires.csv) begin with |N|≥500, |D|≥10) client-server system: python for processing (server), D3 for VIS (client) Task 1: data clustering and decimation implemented 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 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) 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 scatterplot matrix of the three highest PCA loaded attributes
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