Abstract
The paper presents the bottom-up subspace clustering approach and discusses some drawbacks of clustering methods in broad analysis of complex, high-dimensional data. The aim of this paper is to propose some improvements of existing bottom-up subspace clustering methods. A novel grid-based bottom-up subspace clustering algorithm is presented which is able to handle both numerical and nominal attributes and requires only one single parameter. Clusters are represented as hyper-rectangles in sub-spaces of attributes and can be easily interpreted by a human as decision rules. The results of experiments conducted on artificial and real data sets are included
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.
