Abstract
Many distance-related algorithms, such as k- nearest neighbor learning algorithms, locally weighted learn- ing algorithms etc, depend upon a good distance metric to be successful. In this kind of algorithms, a key problem is how to measure the distance between each pair of instances. In this paper, we provide a survey on distance metrics for nominal attributes, including some basic distance metrics and their improvements based on attribute weighting and attribute selection. The experimental results on the whole 36 UCI datasets published on the main web site of Weka platform validate their effectiveness.
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.
