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Anytime Hierarchical Clustering

ESE Technical Report, 2014

arXiv:1404.3439 [stat.ML], 2014

Omur Arslan*, and Daniel E. Koditschek*
*: Electrical and Systems Engineering, University of Pennsylvania
Full PDF | arXiv

Anytime Hierarchical Clustering
Anytime Hierarchical Clustering
Abstract
       We propose a new anytime hierarchical clustering method that iteratively transforms an arbitrary initial hierarchy on the configuration of measurements along a sequence of trees we prove for a fixed data set must terminate in a chain of nested partitions that satisfies a natural homogeneity requirement. Each recursive step re-edits the tree so as to improve a local measure of cluster homogeneity that is compatible with a number of commonly used (e.g., single, average, complete) linkage functions. As an alternative to the standard batch algorithms, we present numerical evidence to suggest that appropriate adaptations of this method can yield decentralized, scalable algorithms suitable for distributed/parallel computation of clustering hierarchies and online tracking of clustering trees applicable to large, dynamically changing databases and anomaly detection.
This work was funded in part by the Air Force Office of Science Research under the MURI FA9550–10–1−0567.
BibTeX entry
@TECHREPORT{Arslan_Kod_AHC_TechReport2014,
author       = {Arslan, O. and Koditschek, D. E.},
title        = {Anytime Hierarchical Clustering},
institution  = {University Of Pennsylvania},
year         = {2014}
}

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