Abstract
In this chapter, we discuss some issues arising when Gaussian Mixture Models (GMMs) are applied for clustering purposes and introduce the most commonly known strategies to solve them. Specifically, we discuss issues related to model-based clustering in high-dimensional spaces and to departures from the nominal model due to skew or contaminated data. Applications to several case studies will be presented.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
