Improving Consensus Hierarchical Clustering Framework

  • Ashis Kumer Biswas
  • Baoju Zhang
  • Xiaoyong Wu
  • Jean X. Gao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 386)


Consensus clustering has been widely accepted in recent years as an effective alternative to the individual clustering. Individual clustering results may contain noise inherent to the original dataset that introduces instability and inconsistency during the interpretation. Consensus clustering strategies focus on combining different individual clustering results from a dataset and generates a single clustering result overcoming the inconsistencies that otherwise could not be avoided. Consensus partitional clustering has been thoroughly studied over the past decade, but only a few contributing research on the consensus hierarchical clustering domain has been done. In this paper, we present an improved and simplified consensus hierarchical clustering framework that is capable of producing a consensus aggregated hierarchical cluster given a set of individual hierarchical clustering results, denoted as dendrograms. Moreover, we propose an additional dendrogram descriptor matrix which is ultra-metric, and improves the consensus clustering performance to some extent than the others. We applied our framework on three generic datasets from the UCI machine learning repository to solve the consensus hierarchical clustering problem and the experimental results demonstrate the effectiveness of our framework.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe University of Texas at ArlingtonArlingtonUSA
  2. 2.School of Communications and Electronic Information, Tianjin Normal UniversityTianjinChina

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