Abstract
As the density peak clustering algorithm is insensitive to local structure, having bad clustering effect on data sets with uneven density distribution, and the algorithm does not take into account the hierarchy of data points, an improvement of density peak clustering algorithm is presented. The contribution factor is introduced into the local density calculation of data points to improve the insensitivity of the original algorithm to the local structure. In addition, the concept of trend is adopted to reduce the allocation joint error caused by the original algorithm only judging the similarity according to the distance of data points, and the method of leading forest is proposed to explain the hierarchy of data points. Finally, the experiments on artificial data sets and UCI datasets show that the improved algorithm is feasible.
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.
