Technical Reports (ESE)

Document Type

Technical Report

Subject Area

Kodlab

Date of this Version

4-13-2014

Comments

For more information: Kod*Lab

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.

Bib Tex

@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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Date Posted: 18 February 2015

This document has been peer reviewed.