2024-03-29T02:05:21Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:001110222023-11-17T02:17:36Z06504:07208:07826
A fast density-based clustering algorithm using fuzzy neighborhood functionsengソフトウェア科学・工学http://id.nii.ac.jp/1001/00110998/Conference Paperhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=111022&item_no=1&attribute_id=1&file_no=1Copyright (c) 2013 by the Information Processing Society of Japan北大北大北大北大劉浩小山聡栗原正仁佐藤晴彦Density-based clustering algorithms, such as DBSCAN, usually have a difficulty in selecting appropriate parameters. Recently, the FN-DBSCAN algorithm extended density-based clustering algorithms with the fuzzy set theory and solved this problem. However, FN-DBSCAN has a time complexity of o(n^2) which indicates that it is not suitable to deal with large scale of data. In this study, we propose a novel clustering algorithm called landmark FN-DBSCAN which has a linear time and space complexity to the size of input data and provides a good quality of clustering.AN00349328第75回全国大会講演論文集201313994002013-03-062014-12-18