IJMLC 2014 Vol.4(3): 275-278 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.424

Cluster Ensembles, Majority Vote, Voter Eligibility and Privileged Voters

Masoud Charkhabi, Tarundeep Dhot, and Shirin A. Mojarad

Abstract—Grouping items facilitates ideation. Although Cluster Analysis has become a classical technique for grouping in science and engineering, to the best of our knowledge it's use remains limited in business. In this application paper we use cluster ensembles to address three barriers to wide scale adoption in the banking industry. The aforementioned challenges are: consistency of results, knowledge beyond the data and grouping with multiple objectives. Contributions of this study include guidance on dealing with the lack of meaningful cluster labels (in the case of ensembles), bimodal cluster distributions and incorporating expert intuition into the clustering process. This application has delivered unobvious insight into a high-dimensional dataset to audiences with diverse backgrounds.

Index Terms—Clustering, cluster ensembles, majority vote.

Masoud Charkhabi, Tarundeep Dhot, and Shirin A. Mojarad are with the Advanced Analytics division of the Canadian Imperial Bank of Commerce. Commerce Court, Toronto, Canada (e-mail: masoud.charkhabi@cibc.com, tarundeep.dhot@cibc.com, shirin.mojarad@cibc.com).

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Cite: Masoud Charkhabi, Tarundeep Dhot, and Shirin A. Mojarad, "Cluster Ensembles, Majority Vote, Voter Eligibility and Privileged Voters," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 275-278, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net