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Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>With a large number of distance measures, the appropriate choice for clustering a given data set with a specified clustering algorithm becomes an important problem. In this article, an automatic distance measure recommendation method for clustering algorithms is proposed. The recommendation method consists of the following steps: (1) metadata extraction, including meta-feature collection and meta-target identification; (2) recommendation model construction using metadata; and (3) distance measure recommendation for a new data set by the recommendation model. Two different types of meta-targets and meta-learning techniques are utilized considering the possible different requirements of users.<\/jats:p>\n          <jats:p>To validate the necessity and effectiveness of the distance measure recommendation method, an empirical study is conducted with 199 publicly available data sets, 9 distance measures, and 2 widely used clustering algorithms. The experimental results indicate that distance measure significantly influences the performance of the clustering algorithm for a given data set. Furthermore, performance analysis of the proposed recommendation method proves its effectiveness.<\/jats:p>","DOI":"10.1145\/3418228","type":"journal-article","created":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T19:04:16Z","timestamp":1607367856000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Recommendation of a Distance Measure for Clustering Algorithms"],"prefix":"10.1145","volume":"15","author":[{"given":"Xiaoyan","family":"Zhu","sequence":"first","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingbin","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayin","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Zheng","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Fu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5687-8"},{"volume-title":"Computational Intelligence: Theories","author":"Aggarwal Swati","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s003579900027"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2006.01.013"},{"key":"e_1_2_1_5_1","first-page":"2501","article-title":"Effect of different distance measures on the performance of K-means algorithm: An experimental study in Matlab","volume":"5","author":"Bora Dibya Jyoti","year":"2014","journal-title":"International Journal of Computer Science and Information Technologies (IJCSIT)"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1021713901879"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106180"},{"volume-title":"CommunityDiff: Visualizing community clustering algorithms. 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