Visualization

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Last edited Jan 1, 2018

Created on Dec 31, 2017

This visualization shows the Davies-Bouldin Index values for K-Means clustering of citation data from the top 2000 papers of the 1990s, with each point representing a different cluster count. A lower DB index indicates better clustering, revealing that 15 clusters achieve a notably better score (5.9) than 2 clusters (9.2). The scatter plot uses d3.svg.axis for axes, d3.scale.linear for positioning, and d3.tip for tooltips, with d3.behavior.zoom enabling interactive panning and zooming. Data comes from a CSV file containing K values and their corresponding DB and W metrics.

AI-generated description

The Davies-Bouldin-Indexes for K-Means-Clustering of the Citation-Data of the top 2000 Papers from the 1990s. A low index indicates good clustering, which means that the clusters have high internal consistency and are very different to each other So we can say that the hypothesis of two clusters (eg. analytic and continental philosophy) seems not particulary merited (DB = 9.2), expecially when compared to 15 clusters, which yields a DB of 5.9.

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