Analysis of the influence of diversity in collaborative and multi-view clustering
Résumé
Multi-source clustering is common data mining task
the aim of which is to use several clustering algorithms to analyze
different aspects of the same data. Well known applications of
multi-source clustering include horizontal collaborative clustering
and multi-view clustering, where several algorithms combine
their strengths by exchanging information about their finding on
local structures with a goal of mutual improvement. However,
many of these proposed algorithms and statistical models lack
the capability to detect weak collaborations that may prove
detrimental to the global clustering process.
In this article, we propose a weighing optimization method
that will help detecting which algorithms should exchange their
information based on the diversity between the different algorithms’
solutions.