Abstract
Data clustering is a data exploration technique that allows objects with similar characteristics to be grouped together in order to facilitate their further processing. The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and an improvement of the existing k-means algorithm to assist the selection is proposed. The paper concludes with an analysis of the results of using cluster validation referring to some measures that are classified as internal and external indexes to determine the optimal number of clusters for the K-means algorithm. There are applied some stopping criterion referring to those indexes for evaluating a clustering against a gold standart.
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.
