{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:00:12Z","timestamp":1764784812183},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tkde.2022.3144914","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T20:37:21Z","timestamp":1643143041000},"page":"1-1","source":"Crossref","is-referenced-by-count":9,"title":["Point-Set Kernel Clustering"],"prefix":"10.1109","author":[{"given":"Kai Ming","family":"Ting","sequence":"first","affiliation":[]},{"given":"Jonathan R","family":"Wells","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1242072"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.5555\/3001460.3001507"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.07.007"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014755"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219990"},{"key":"ref6","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume-title":"Proc. 5th Berkeley Symp. Math. Statist. Probability","author":"MacQueen"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-75225-7_5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1561\/2200000060"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389"},{"key":"ref10","first-page":"682","article-title":"Using the Nystr\u00f6m method to speed up kernel machines","volume-title":"Proc. Adv. Neural Informat. Process. Syst. 13","author":"Williams"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/1137856.1137880"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"issue":"12","key":"ref13","first-page":"1","article-title":"Scalable kernel k-means clustering with nystr\u00f6m approximation: Relative-error bounds","volume":"20","author":"Wang","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2780166"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-14142-8"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3061660"},{"key":"ref17","volume-title":"Introduction to Data Mining","author":"Tan","year":"2018"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1700770114"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312186"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.06.012"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.5187"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2359662"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2746539.2746553"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3068335"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2007.18"},{"key":"ref26","first-page":"476","article-title":"Nystr\u00f6m method vs random fourier features: A theoretical and empirical comparison","volume-title":"Proc. 25th Int. Conf. Neural Informat. Process. Syst.","author":"Yang"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014118"},{"key":"ref29","first-page":"305","article-title":"Learning spectral clustering","volume-title":"Proc. Adv. Neural Inf. Process. Syst. 16","author":"Bach"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1814462116"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1115"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-1115-1"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1238-7"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7496.003.0015"},{"key":"ref35","volume-title":"The WEKA Workbench. Online Appendix for \u2018Data Mining: Practical Machine Learning Tools and Techniques\u2019","author":"Frank","year":"2016"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/BF00131148"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-7152(96)00213-1"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312186"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/4358933\/09693135.pdf?arnumber=9693135","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T22:37:08Z","timestamp":1705185428000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9693135\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/tkde.2022.3144914","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}