Page 31 - Data Science Algorithms in a Week

16 Ramazan Ünlü

result. By internal quality measures, they refer to the real-valued quality metrics that are
computed directly from a clustering and do not include calculations that involve data
sample class information as opposed to external quality measures (Ünlü & Xanthopoulos,
2016b). In the next step, they have tried to make this study better in terms of a well-
known evaluation metric; variance. They have optimized internal quality measures by
applying Markowitz Portfolio Theory (MPT). Using the core idea of MPT which is
constructing portfolios to optimize expected return based on a given level of market risk
which is considered as variance, they have taken not only value of the validity measures
itself but variation on them into consideration. By doing this, they aimed to reduce
variance of the accuracy of the final partition which is produced by weighted consensus
clustering (Ünlü & Xanthopoulos, 2016a).
Throughout the section, some featured studies have been summarized. Researches on
consensus clustering are not limited to those summarized above, other contributions can
be seen in (Berikov, 2014; Gupta & Verma, 2014; Kang, Liu, Zhou, & Li, 2016; Lock &
Dunson, 2013; Parvin, Minaei-Bidgoli, Alinejad-Rokny, & Punch, 2013; Su, Shang, &
Shen, 2015; Wang, Shan, & Banerjee, 2011; Wu, Liu, Xiong, & Cao, 2013).

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