{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T02:22:39Z","timestamp":1772418159058,"version":"3.50.1"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2009,11,1]],"date-time":"2009-11-01T00:00:00Z","timestamp":1257033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2009,11]]},"abstract":"<jats:p>\n            Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams. In evolutionary clustering, a good clustering result should fit the current data well, while simultaneously not deviate too dramatically from the recent history. To fulfill this dual purpose, a measure of\n            <jats:italic>temporal smoothness<\/jats:italic>\n            is integrated in the overall measure of clustering quality. In this article, we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering. For both frameworks, we start with intuitions gained from the well-known\n            <jats:italic>k<\/jats:italic>\n            -means clustering problem, and then propose and solve corresponding cost functions for the evolutionary spectral clustering problems. Our solutions to the evolutionary spectral clustering problems provide more stable and consistent clustering results that are less sensitive to short-term noises while at the same time are adaptive to long-term cluster drifts. Furthermore, we demonstrate that our methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary\n            <jats:italic>k<\/jats:italic>\n            -means clustering problems. Performance experiments over a number of real and synthetic data sets illustrate our evolutionary spectral clustering methods provide more robust clustering results that are not sensitive to noise and can adapt to data drifts.\n          <\/jats:p>","DOI":"10.1145\/1631162.1631165","type":"journal-article","created":{"date-parts":[[2009,12,8]],"date-time":"2009-12-08T20:53:14Z","timestamp":1260305594000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":105,"title":["On evolutionary spectral clustering"],"prefix":"10.1145","volume":"3","author":[{"given":"Yun","family":"Chi","sequence":"first","affiliation":[{"name":"NEC Laboratories America, Cupertino, CA"}]},{"given":"Xiaodan","family":"Song","sequence":"additional","affiliation":[{"name":"Google Inc., Mountain View, CA"}]},{"given":"Dengyong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA"}]},{"given":"Koji","family":"Hino","sequence":"additional","affiliation":[{"name":"NEC Laboratories America, Cupertino, CA"}]},{"given":"Belle L.","family":"Tseng","sequence":"additional","affiliation":[{"name":"YAHOO! Inc., Santa Clara, CA"}]}],"member":"320","published-online":{"date-parts":[[2009,12,4]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 12th VLDB Conference.","author":"Aggarwal C. C."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281290"},{"key":"e_1_2_1_3_1","unstructured":"Bach F. R. and Jordan M. I. 2006. Learning spectral clustering with application to speech separation. J. Mach. Learn. Res. 7.   Bach F. R. and Jordan M. I. 2006. Learning spectral clustering with application to speech separation. J. Mach. Learn. 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On spectral clustering: Analysis and an algorithm. In NIPS.  Ng A. Jordan M. and Weiss Y. 2001. On spectral clustering: Analysis and an algorithm. In NIPS."},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining. SIAM","author":"Ning H."},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Palla G. Barabasi A.-L. and Vicsek T. 2007. Quantifying social group evolution. Nature 446.  Palla G. Barabasi A.-L. and Vicsek T. 2007. Quantifying social group evolution. 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