{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-internal-validity-index-based-on-density","title":"An Internal Validity Index Based on Density-Involved Distance","arxiv_id":null,"date":"2019-03-22","proceeding":null,"authors":["Lianyu Hu","Caiming Zhong"],"abstract":"It is crucial to evaluate the quality of clustering results in cluster analysis. Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when dealing with non-spherical datasets. One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters. In this paper, a new robust distance measure, one into which density is incorporated, is designed to solve the problem, and an internal validity index based on this separation measure is then proposed. This index can cope with both the spherical and non-spherical structure of clusters. The experimental results indicate that the proposed index outperforms some classical CVIs. The MATLAB code and experimental data are available at https://github.com/hulianyu/CVDD","url_abs":"https://ieeexplore.ieee.org/document/8672850","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8672850","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-internal-validity-index-based-on-density","repo_url":"https://github.com/hulianyu/CVDD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"clustering-algorithms-evaluation","task_name":"Clustering Algorithms Evaluation"},{"task_slug":"clustering-ensemble","task_name":"Clustering Ensemble"},{"task_slug":"face-clustering","task_name":"Face Clustering"},{"task_slug":"graph-clustering","task_name":"Graph Clustering"},{"task_slug":"imagedocument-clustering","task_name":"Image/Document Clustering"},{"task_slug":"spectral-graph-clustering","task_name":"Spectral Graph Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/clustering-algorithms-evaluation-on-97","task":"Clustering Algorithms Evaluation","dataset":"97 synthetic datasets","model":"CVDD","rank_in_archive_order":1,"of":2,"metrics":{"HIT-THE-BEST":"50","Rank difference":"337"},"uses_additional_data":false},{"leaderboard":"/sota/clustering-algorithms-evaluation-on-jaffe","task":"Clustering Algorithms Evaluation","dataset":"JAFFE","model":"CVDD","rank_in_archive_order":1,"of":1,"metrics":{"Purity":"0.977"},"uses_additional_data":false},{"leaderboard":"/sota/clustering-algorithms-evaluation-on","task":"Clustering Algorithms Evaluation","dataset":"ionosphere","model":"CVDD","rank_in_archive_order":1,"of":1,"metrics":{"Purity":"0.843"},"uses_additional_data":false},{"leaderboard":"/sota/clustering-algorithms-evaluation-on-iris","task":"Clustering Algorithms Evaluation","dataset":"iris","model":"CVDD","rank_in_archive_order":1,"of":1,"metrics":{"Purity":"0.967"},"uses_additional_data":false},{"leaderboard":"/sota/clustering-algorithms-evaluation-on-pathbased","task":"Clustering Algorithms Evaluation","dataset":"pathbased","model":"CVDD","rank_in_archive_order":1,"of":1,"metrics":{"Purity":"0.977"},"uses_additional_data":false},{"leaderboard":"/sota/clustering-algorithms-evaluation-on-pixraw10p","task":"Clustering Algorithms Evaluation","dataset":"pixraw10P","model":"CVDD","rank_in_archive_order":1,"of":1,"metrics":{"Purity":"0.83"},"uses_additional_data":false},{"leaderboard":"/sota/clustering-algorithms-evaluation-on-seeds","task":"Clustering Algorithms Evaluation","dataset":"seeds","model":"CVDD","rank_in_archive_order":1,"of":1,"metrics":{"Purity":"0.905"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}