Improving Consensus Hierarchical Clustering Framework

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 386)

Abstract

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.

References

  1. 1.
    Fred AL, Jain AK (2005) Combining multiple clusterings using evidence accumulation. IEEE Trans Pattern Anal Mach Intell 27(6):835–850CrossRefGoogle Scholar
  2. 2.
    Hossain M, Bridges SM, Wang Y, Hodges JE (2012) An effective ensemble method for hierarchical clustering. In: Proceedings of the fifth international C* conference on computer science and software engineering, pp. 18–26. ACMGoogle Scholar
  3. 3.
    Li T, Ding C (2008) Weighted consensus clustering, pp. 798–809. SIAMGoogle Scholar
  4. 4.
    Mirzaei A, Rahmati M (2008) Combining hierarchical clusterings using min-transitive closure. In: 19th international conference on pattern recognition, 2008. ICPR 2008, pp. 1–4. IEEEGoogle Scholar
  5. 5.
    Mirzaei A, Rahmati M (2010) A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations. IEEE Trans Fuzzy Syst 18(1):27–39CrossRefGoogle Scholar
  6. 6.
    Podani J (2000) Simulation of random dendrograms and comparison tests: some comments. J Classif 17(1):123–142MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Rashedi E, Mirzaei A, Rahmati M (2015) An information theoretic approach to hierarchical clustering combination. Neurocomputing 148:487–497CrossRefGoogle Scholar
  8. 8.
    Strehl A, Ghosh J (2003) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583–617MathSciNetMATHGoogle Scholar
  9. 9.
    Zheng L, Li T, Ding C (2014) A framework for hierarchical ensemble clustering. ACM Trans Knowl Discov Data (TKDD) 9(2):9Google Scholar

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

Personalised recommendations