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Uncovering Group Level Insights with Accordant Clustering
Clustering is a widely-used data mining tool, which aims to discover
partitions of similar items in data. We introduce a new clustering paradigm,
\emph{accordant clustering}, which enables the discovery of (predefined) group
level insights. Unlike previous clustering paradigms that aim to understand
relationships amongst the individual members, the goal of accordant clustering
is to uncover insights at the group level through the analysis of their
members. Group level insight can often support a call to action that cannot be
informed through previous clustering techniques. We propose the first accordant
clustering algorithm, and prove that it finds near-optimal solutions when data
possesses inherent cluster structure. The insights revealed by accordant
clusterings enabled experts in the field of medicine to isolate successful
treatments for a neurodegenerative disease, and those in finance to discover
patterns of unnecessary spending.Comment: accepted to SDM 2017 (oral
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