Active clustering: Robust and efficient hierarchical clustering using adaptively selected similarities

Brian Eriksson, Gautam Dasarathy, Aarti Singh, Robert Nowak

Research output: Contribution to journalConference article

12 Scopus citations

Abstract

Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small subset of pairwise similarities, significantly less than the complete set of N(N - 1)/2 similarities. First, we show that if the intracluster similarities exceed intercluster similarities, then it is possible to correctly determine the hierarchical clustering from as few as 3N logN similarities. We demonstrate this order of magnitude savings in the number of pairwise similarities necessitates sequentially selecting which similarities to obtain in an adaptive fashion, rather than picking them at random. We then propose an active clustering method that is robust to a limited fraction of anomalous similarities, and show how even in the presence of these noisy similarity values we can resolve the hierarchical clustering using only O (N log 2 N) pairwise similarities.

Original languageEnglish (US)
Pages (from-to)260-268
Number of pages9
JournalJournal of Machine Learning Research
Volume15
StatePublished - Dec 1 2011
Externally publishedYes
Event14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011 - Fort Lauderdale, FL, United States
Duration: Apr 11 2011Apr 13 2011

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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