{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:24:18Z","timestamp":1758266658616,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031001253"},{"type":"electronic","value":"9783031001260"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-00126-0_40","type":"book-chapter","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T18:07:55Z","timestamp":1650996475000},"page":"556-573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LiteWSC: A Lightweight Framework for Web-Scale Spectral Clustering"],"prefix":"10.1007","author":[{"given":"Geping","family":"Yang","sequence":"first","affiliation":[]},{"given":"Sucheng","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Yiyang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhiguo","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"issue":"8","key":"40_CR1","first-page":"1669","volume":"45","author":"D Cai","year":"2015","unstructured":"Cai, D., Chen, X.: Large scale spectral clustering via landmark-based sparse representation. TCYB 45(8), 1669\u20131680 (2015)","journal-title":"TCYB"},{"issue":"12","key":"40_CR2","first-page":"1624","volume":"17","author":"D Cai","year":"2005","unstructured":"Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. TKDE 17(12), 1624\u20131637 (2005)","journal-title":"TKDE"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1\u201327:27 (2011)","DOI":"10.1145\/1961189.1961199"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Chen, P., Wu, L.: Revisiting spectral graph clustering with generative community models. In: Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X. (eds.) ICDM, pp. 51\u201360 (2017)","DOI":"10.1109\/ICDM.2017.14"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Chen, X., Cai, D.: Large scale spectral clustering with landmark-based representation. In: AAAI (2011)","DOI":"10.1609\/aaai.v25i1.7900"},{"key":"40_CR6","unstructured":"Chung, F.R., Graham, F.C.: Spectral Graph Theory, no. 92. American Mathematical Society (1997)"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: EMNIST: extending MNIST to handwritten letters. In: IJCNN, pp. 2921\u20132926. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"40_CR8","unstructured":"Couillet, R., Chatelain, F., Bihan, N.L.: Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering. In: ICML, vol. 139, pp. 2156\u20132165 (2021)"},{"issue":"11","key":"40_CR9","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1109\/TPAMI.2007.1115","volume":"29","author":"IS Dhillon","year":"2007","unstructured":"Dhillon, I.S., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors a multilevel approach. TPAMI 29(11), 1944\u20131957 (2007)","journal-title":"TPAMI"},{"issue":"5","key":"40_CR10","doi-asserted-by":"publisher","first-page":"2197","DOI":"10.1073\/pnas.0437847100","volume":"100","author":"DL Donoho","year":"2003","unstructured":"Donoho, D.L., Elad, M.: Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization. PNAS 100(5), 2197\u20132202 (2003)","journal-title":"PNAS"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Duan, L., Aggarwal, C.C., Ma, S., Sathe, S.: Improving spectral clustering with deep embedding and cluster estimation. In: ICDM, pp. 170\u2013179. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00027"},{"issue":"2","key":"40_CR12","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1109\/TPAMI.2004.1262185","volume":"26","author":"CC Fowlkes","year":"2004","unstructured":"Fowlkes, C.C., Belongie, S.J., Chung, F.R.K., Malik, J.: Spectral grouping using the Nystr\u00f6m method. TPAMI 26(2), 214\u2013225 (2004)","journal-title":"TPAMI"},{"issue":"6","key":"40_CR13","first-page":"2325","volume":"44","author":"RM Gray","year":"1998","unstructured":"Gray, R.M., Neuhoff, D.L.: Quantization. TIT 44(6), 2325\u20132383 (1998)","journal-title":"Quantization. TIT"},{"key":"40_CR14","unstructured":"Haeffele, B.D., You, C., Vidal, R.: A critique of self-expressive deep subspace clustering. In: ICLR (2021)"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100\u2013108 (1979)","DOI":"10.2307\/2346830"},{"issue":"6","key":"40_CR16","first-page":"1212","volume":"32","author":"D Huang","year":"2020","unstructured":"Huang, D., Wang, C., Wu, J., Lai, J., Kwoh, C.: Ultra-scalable spectral clustering and ensemble clustering. TKDE 32(6), 1212\u20131226 (2020)","journal-title":"TKDE"},{"key":"40_CR17","unstructured":"Huang, L., Yan, D., Jordan, M.I., Taft, N.: Spectral clustering with perturbed data. In: NIPS. pp. 705\u2013712. Curran Associates, Inc. (2008)"},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Lapata, M., Blunsom, P., Koller, A. (eds.) EACL, pp. 427\u2013431. ACL (2017)","DOI":"10.18653\/v1\/E17-2068"},{"key":"40_CR19","unstructured":"Lin, F., Cohen, W.W.: Power iteration clustering. In: F\u00fcrnkranz, J., Joachims, T. (eds.) ICML, pp. 655\u2013662. Omnipress (2010)"},{"issue":"4","key":"40_CR20","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U von Luxburg","year":"2007","unstructured":"von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395\u2013416 (2007)","journal-title":"Stat. Comput."},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Mohan, M., Monteleoni, C.: Beyond the Nystr\u00f6m approximation: speeding up spectral clustering using uniform sampling and weighted kernel k-means. In: IJCAI (2017)","DOI":"10.24963\/ijcai.2017\/347"},{"key":"40_CR22","first-page":"849","volume":"2","author":"AY Ng","year":"2002","unstructured":"Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: analysis and an algorithm. NIPS 2, 849\u2013856 (2002)","journal-title":"NIPS"},{"issue":"6583","key":"40_CR23","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/381607a0","volume":"381","author":"BA Olshausen","year":"1996","unstructured":"Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607\u2013609 (1996)","journal-title":"Nature"},{"issue":"12","key":"40_CR24","first-page":"5379","volume":"24","author":"Z Pan","year":"2015","unstructured":"Pan, Z., Fan, H., Zhang, L.: Texture classification using local pattern based on vector quantization. TIP 24(12), 5379\u20135388 (2015)","journal-title":"TIP"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"Sculley, D.: Web-scale k-means clustering. In: Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (eds.) WWW, pp. 1177\u20131178. ACM (2010)","DOI":"10.1145\/1772690.1772862"},{"issue":"8","key":"40_CR26","first-page":"888","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi, J., Malik, J.: Normalized cuts and image segmentation. TPAMI 22(8), 888\u2013905 (2000)","journal-title":"Normalized cuts and image segmentation. TPAMI"},{"key":"40_CR27","volume-title":"Introduction to Matrix Computations","author":"GW Stewart","year":"1973","unstructured":"Stewart, G.W.: Introduction to Matrix Computations. Academic Press, Cambridge (1973)"},{"key":"40_CR28","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl, A., Ghosh, J.: Cluster ensembles \u2013 a knowledge reuse framework for combining multiple partitions. JMLR 3, 583\u2013617 (2002)","journal-title":"JMLR"},{"key":"40_CR29","unstructured":"Vladymyrov, M., Carreira-Perpinan, M.: The variational Nystrom method for large-scale spectral problems. In: ICML, pp. 211\u2013220. PMLR (2016)"},{"issue":"1","key":"40_CR30","first-page":"2729","volume":"14","author":"S Wang","year":"2013","unstructured":"Wang, S., Zhang, Z.: Improving CUR matrix decomposition and the Nystr\u00f6m approximation via adaptive sampling. JMLR 14(1), 2729\u20132769 (2013)","journal-title":"JMLR"},{"key":"40_CR31","unstructured":"Williams, C.K.I., Seeger, M.W.: Using the Nystr\u00f6m method to speed up kernel machines. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) NIPS, pp. 682\u2013688. MIT Press (2000)"},{"key":"40_CR32","doi-asserted-by":"crossref","unstructured":"Yan, D., Huang, L., Jordan, M.: Fast approximate spectral clustering. Technical report UCB\/EECS-2009-45, EECS Department, University of California, Berkeley, March 2009","DOI":"10.1145\/1557019.1557118"},{"key":"40_CR33","doi-asserted-by":"crossref","unstructured":"Yan, D., Huang, L., Jordan, M.I.: Fast approximate spectral clustering. In: IV, J.F.E., Fogelman-Souli\u00e9, F., Flach, P.A., Zaki, M.J. (eds.) KDD, pp. 907\u2013916. ACM (2009)","DOI":"10.1145\/1557019.1557118"},{"key":"40_CR34","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.ins.2020.07.018","volume":"544","author":"Y Yang","year":"2021","unstructured":"Yang, Y., et al.: GraphLSHC: towards large scale spectral hypergraph clustering. Inf. Sci. 544, 117\u2013134 (2021)","journal-title":"Inf. Sci."},{"issue":"2","key":"40_CR35","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/TIT.1982.1056490","volume":"28","author":"PL Zador","year":"1982","unstructured":"Zador, P.L.: Asymptotic quantization error of continuous signals and the quantization dimension. IEEE Trans. Inf. Theory 28(2), 139\u2013148 (1982)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"40_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/978-3-030-73197-7_20","volume-title":"Database Systems for Advanced Applications","author":"G-Y Zhang","year":"2021","unstructured":"Zhang, G.-Y., Chen, X.-W., Zhou, Y.-R., Wang, C.-D., Huang, D.: Consistency- and Inconsistency-Aware Multi-view Subspace Clustering. In: Jensen, C.S., et al. (eds.) DASFAA 2021. LNCS, vol. 12682, pp. 291\u2013306. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-73197-7_20"},{"issue":"10","key":"40_CR37","first-page":"1576","volume":"21","author":"K Zhang","year":"2010","unstructured":"Zhang, K., Kwok, J.T.: Clustered Nystr\u00f6m method for large scale manifold learning and dimension reduction. TNN 21(10), 1576\u20131587 (2010)","journal-title":"TNN"},{"key":"40_CR38","unstructured":"Zhang, Z., Lange, K., Xu, J.: Simple and scalable sparse k-means clustering via feature ranking. In: NeurIPS (2020)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-00126-0_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T16:08:58Z","timestamp":1675440538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-00126-0_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031001253","9783031001260"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-00126-0_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2022.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"543","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"76","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was originally planned to take place in Hyberabad, India. 24 other papers are included in the volume.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}