Affiliations: [a] Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran | [b] Computer Science and Technology Department, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Hosein Alizadeh, Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. E-mail:[email protected]
Abstract: The Wisdom of Crowds is a phenomenon described in social science that
suggests four criteria applicable to groups of people. It is claimed that,
if these criteria are satisfied, then the aggregate decisions made by a
group will often be better than those of its individual members. Inspired by
this concept, we present a novel feedback framework for the cluster ensemble
problem, which we call Wisdom of Crowds Cluster Ensemble (WOCCE). Although
many conventional cluster ensemble methods focusing on diversity have
recently been proposed, WOCCE analyzes the conditions necessary for a crowd
to exhibit this collective wisdom. These include decentralization criteria
for generating primary results, independence criteria for the base
algorithms, and diversity criteria for the ensemble members. We suggest
appropriate procedures for evaluating these measures, and propose a new
measure to assess the diversity. We evaluate the performance of WOCCE
against some other traditional base algorithms as well as state-of-the-art
ensemble methods. The results demonstrate the efficiency of WOCCE's
aggregate decision-making compared to other algorithms.
Keywords: Cluster ensemble, Wisdom of Crowds, diversity, independency, K-means
