Abstract

AbstractAn original on-line mixture model-based clustering algorithm is presented in this paper. The proposed algorithm is a stochastic gradient ascent derived from the Classification EM (CEM) algorithm. It generalizes the on-line k-means algorithm. Using synthetic data sets, the proposed algorithm is compared to CEM and another on-line clustering algorithm. The results show that the proposed method provides a fast and accurate estimation when mixture components are relatively well separated.KeywordsAcoustic EmissionMixture ModelLearning RateExponential FamilyLikelihood CriterionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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 call

Disclaimer: 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.