{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T04:11:30Z","timestamp":1748751090427,"version":"3.41.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319256597"},{"type":"electronic","value":"9783319256603"}],"license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"DOI":"10.1007\/978-3-319-25660-3_17","type":"book-chapter","created":{"date-parts":[[2015,11,25]],"date-time":"2015-11-25T12:11:58Z","timestamp":1448453518000},"page":"198-209","source":"Crossref","is-referenced-by-count":4,"title":["Internal Clustering Evaluation of Data Streams"],"prefix":"10.1007","author":[{"given":"Marwan","family":"Hassani","sequence":"first","affiliation":[]},{"given":"Thomas","family":"Seidl","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,11,26]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: VLDB, pp. 81\u201392 (2003)","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"17_CR2","first-page":"44","volume":"11","author":"A Bifet","year":"2010","unstructured":"Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T., Seidl, T.: MOA: massive online analysis, a framework for stream classification and clustering. JMLR 11, 44\u201350 (2010)","journal-title":"JMLR"},{"issue":"1","key":"17_CR3","first-page":"1","volume":"3","author":"T Calinski","year":"1974","unstructured":"Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1\u201327 (1974)","journal-title":"Commun. Stat."},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: SIAM SDM, pp. 328\u2013339 (2006)","DOI":"10.1137\/1.9781611972764.29"},{"issue":"2","key":"17_CR5","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"1","author":"D Davies","year":"1979","unstructured":"Davies, D., Bouldin, D.: A cluster separation measure. IEEE PAMI 1(2), 224\u2013227 (1979)","journal-title":"IEEE PAMI"},{"issue":"1","key":"17_CR6","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/01969727408546059","volume":"4","author":"J Dunn","year":"1974","unstructured":"Dunn, J.: Well separated clusters and optimal fuzzy partitions. J. Cybern. 4(1), 95\u2013104 (1974)","journal-title":"J. Cybern."},{"issue":"2","key":"17_CR7","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1023\/A:1012801612483","volume":"17","author":"M Halkidi","year":"2001","unstructured":"Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2), 107\u2013145 (2001)","journal-title":"J. Intell. Inf. Syst."},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Halkidi, M., Vazirgiannis, M.: Clustering validity assessment: finding the optimal partitioning of a data set. In IEEE ICDM, pp. 187\u2013194 (2001)","DOI":"10.1109\/ICDM.2001.989517"},{"key":"17_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/3-540-45372-5_26","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"M Vazirgiannis","year":"2000","unstructured":"Vazirgiannis, M., Halkidi, M., Batistakis, Y.: Quality scheme assessment in the clustering process. In: \u017bytkow, J.M., Zighed, D.A., Komorowski, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 265\u2013276. Springer, Heidelberg (2000)"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Hassani, M., Kim, Y., Seidl, T.: Subspace MOA: subspace stream clustering evaluation using the MOA framework. In: DASFAA, pp. 446\u2013449 (2013)","DOI":"10.1007\/978-3-642-37450-0_33"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Hassani, M., Kranen, P., Saini, R., Seidl, T.: Subspace anytime stream clustering. In: SSDBM, p. 37 (2014)","DOI":"10.1145\/2618243.2618286"},{"key":"17_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-642-33362-0_24","volume-title":"Scalable Uncertainty Management","author":"M Hassani","year":"2012","unstructured":"Hassani, M., Spaus, P., Gaber, M.M., Seidl, T.: Density-based projected clustering of data streams. In: Link, S., Fober, T., Seeger, B., H\u00fcllermeier, E. (eds.) SUM 2012. LNCS, vol. 7520, pp. 311\u2013324. Springer, Heidelberg (2012)"},{"key":"17_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1007\/978-3-319-08979-9_11","volume-title":"Machine Learning and Data Mining in Pattern Recognition","author":"M Hassani","year":"2014","unstructured":"Hassani, M., Spaus, P., Seidl, T.: Adaptive multiple-resolution stream clustering. In: Perner, P. (ed.) MLDM 2014. LNCS, vol. 8556, pp. 134\u2013148. Springer, Heidelberg (2014)"},{"issue":"1","key":"17_CR14","first-page":"193","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., Arabie, P.: Comparing partitions. J. Intell. Inf. Syst. 2(1), 193\u2013218 (1985)","journal-title":"J. Intell. Inf. Syst."},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Kremer, H., Kranen, P., Jansen, T., Seidl, T., Bifet, A., Holmes, G., Pfahringer, B.: An effective evaluation measure for clustering on evolving data streams. In: ACM SIGKDD, pp. 868\u2013876 (2011)","DOI":"10.1145\/2020408.2020555"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: ICDM, pp. 911\u2013916 (2010)","DOI":"10.1109\/ICDM.2010.35"},{"issue":"3","key":"17_CR17","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TSMCB.2012.2220543","volume":"43","author":"Y Liu","year":"2013","unstructured":"Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J., Wu, S.: Understanding and enhancement of internal clustering validation measures. IEEE Trans. Cybern. 43(3), 982\u2013994 (2013)","journal-title":"IEEE Trans. Cybern."},{"key":"17_CR18","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1109\/TPAMI.2002.1114856","volume":"24","author":"U Maulik","year":"2002","unstructured":"Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE PAMI 24, 1650\u20131654 (2002)","journal-title":"IEEE PAMI"},{"issue":"1","key":"17_CR19","first-page":"27","volume":"5","author":"E Rend\u00f3n","year":"2011","unstructured":"Rend\u00f3n, E., Abundez, I., Arizmendi, A., Quiroz, E.M.: Internal versus external cluster validation indexes. Int. J. Comput. Commun. 5(1), 27\u201334 (2011)","journal-title":"Int. J. Comput. Commun."},{"issue":"3\u20134","key":"17_CR20","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/S0167-8655(97)00168-2","volume":"19","author":"MR Rezaee","year":"1998","unstructured":"Rezaee, M.R., Lelieveldt, B.B.F., Reiber, J.H.C.: A new cluster validity index for the fuzzy c-mean. Pattern Recogn. Lett. 19(3\u20134), 237\u2013246 (1998)","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"17_CR21","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"P Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"key":"17_CR22","volume-title":"Introduction to Data Mining","author":"P-N Tan","year":"2005","unstructured":"Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley Longman Inc., Boston (2005)"},{"issue":"8","key":"17_CR23","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/34.85677","volume":"13","author":"XL Xie","year":"1991","unstructured":"Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE PAMI 13(8), 841\u2013847 (1991)","journal-title":"IEEE PAMI"}],"container-title":["Lecture Notes in Computer Science","Trends and Applications in Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-25660-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T14:38:37Z","timestamp":1748702317000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-25660-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"ISBN":["9783319256597","9783319256603"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-25660-3_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2015]]}}}