Page 16 - Data Science Algorithms in a Week

In: Artificial Intelligence ISBN: 978-1-53612-677-8
Editors: L. Rabelo, S. Bhide and E. Gutierrez © 2018 Nova Science Publishers, Inc.

Chapter 1

UNSUPERVISED ENSEMBLE LEARNING

*
Ramazan Ünlü
Industrial Engineering and Management Systems,
University of Central Florida, Orlando, FL, US

ABSTRACT

Clustering is used in identifying groups of samples with similar properties, and it is
one of the most common preliminary exploratory analysis for revealing “hidden”
patterns, in particular for datasets where label information is unknown. Even though
clustering techniques have been well used to analyze a variety of datasets in different
domains for years, the limitation of them is that each clustering method works better only
in certain conditions. This made the selection of the most suitable algorithm for particular
dataset much more important. Restrained implementation of clustering methods has
forced clustering practitioners to develop more robust methods, which is reasonably
practicable in any condition. The unsupervised ensemble learning, or consensus
clustering, is developed to serve this purpose. It consists of finding the optimal
combination strategy of individual partitions that is robust in comparison to the selection
of an algorithmic clustering pool. The goal of this combination process is to improve the
average quality of individual clustering methods. Due to increasing development of new
methods, their promising results and the great number of applications, it is considered to
make a crucial and a brief review about it. Through this chapter, first the main concepts
of clustering methods are briefly introduced and then the basics of ensemble learning is
given. Finally, the chapter is concluded with a comprehensive summary of novel
developments in the area.

Keywords: consensus clustering, unsupervised ensemble learning

* Corresponding Author Email: ramazanunlu@gmail.com.