Abstract
K-means is one of the most popular and simple clustering algorithm. In spite of the fact that K-means was proposed over 60 years ago, it is still widely used. This paper provides a soft assignment K-means algorithm which is an extension of K-means where each data point can be a member of multiple clusters with a membership value. As an example, this paper apply soft assignment K-means algorithm to estimate the parameters of Gaussian mixture models and compare it with traditional K-means algorithm. Experiments demonstrate that soft assignment K-means algorithm can give more accurate result than traditional K-means algorithm which using hard assignment mechanism.
Sign-in/Register to access full text options 
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.
