  Network measurements are an important tool in understanding the Internet. Due
to the expanse of the IPv6 address space, exhaustive scans as in IPv4 are not
possible for IPv6. In recent years, several studies have proposed the use of
target lists of IPv6 addresses, called IPv6 hitlists.
  In this paper, we show that addresses in IPv6 hitlists are heavily clustered.
We present novel techniques that allow IPv6 hitlists to be pushed from quantity
to quality. We perform a longitudinal active measurement study over 6 months,
targeting more than 50 M addresses. We develop a rigorous method to detect
aliased prefixes, which identifies 1.5 % of our prefixes as aliased, pertaining
to about half of our target addresses. Using entropy clustering, we group the
entire hitlist into just 6 distinct addressing schemes. Furthermore, we perform
client measurements by leveraging crowdsourcing.
  To encourage reproducibility in network measurement research and to serve as
a starting point for future IPv6 studies, we publish source code, analysis
tools, and data.
