<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Clustering on Shermstats</title>
    <link>https://www.shermstats.com/tags/clustering/</link>
    <description>Recent content in Clustering on Shermstats</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en-us</language>
    <lastBuildDate>Sun, 22 Jul 2018 00:00:00 +0000</lastBuildDate>
    
	<atom:link href="https://www.shermstats.com/tags/clustering/index.xml" rel="self" type="application/rss+xml" />
    
    
    <item>
      <title>Musical Analysis of Rush Part 2:  Clustering Rush Albums</title>
      <link>https://www.shermstats.com/2018/07/22/musical-analysis-of-rush-part-2-clustering-rush-albums/</link>
      <pubDate>Sun, 22 Jul 2018 00:00:00 +0000</pubDate>
      
      <guid>https://www.shermstats.com/2018/07/22/musical-analysis-of-rush-part-2-clustering-rush-albums/</guid>
      <description>Now that we’ve looked at Rush’s musical development over time, let’s do some hierarchical clustering based on music features. This is heavily inspired by, if not plagiaried, directly from Alyssa Goldberg’s similar analysis on David Bowie albums.
library(ggplot2) library(dplyr) library(tidyr) library(tibble) library(here) library(rpart) library(dendextend) ## for colors library(circlize) ## one of the only times where circles are nice library(spotifyr) source(here(&amp;quot;lib&amp;quot;, &amp;quot;vars.R&amp;quot;)) album_stats &amp;lt;- readRDS(ALBUM_STATS) First of all, our data is in long format where each row is “musical feature of an album”.</description>
    </item>
    
  </channel>
</rss>