Abstract
A new classification algorithm corresponding to a generalization of the K-means algorithm is proposed, whose algorithm is named as a weighted K-means algorithm. Weight coefficients, which provide weighted distortions between data and cluster centers, are incorporated into the algorithm to realize reliable classification. A method determining the appropriate values of the weight coefficients from class labeled data is introduced. Under the situations where statistical distributions of data are changing gradually with time, the weighted K-means algorithm for semi-supervised data composed from initial labeled data and succeeding unlabeled data is investigated.
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.
