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Vol. 520. 2010: Addison-Wesley Reading."},{"key":"e_1_3_2_1_8_1","volume-title":"The use of hierarchic clustering in information retrieval. Information storage and retrieval","author":"Jardine N.","year":"1971","unstructured":"Jardine , N. and C.J. van Rijsbergen , The use of hierarchic clustering in information retrieval. Information storage and retrieval , 1971 . 7(5): p. 217--240. Jardine, N. and C.J. van Rijsbergen, The use of hierarchic clustering in information retrieval. Information storage and retrieval, 1971. 7(5): p. 217--240."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-714250-0.50005-X"},{"key":"e_1_3_2_1_10_1","volume-title":"Pattern recognition principles","author":"Tou J.T.","year":"1974","unstructured":"Tou , J.T. and R.C. Gonzalez , Pattern recognition principles . 1974 . Tou, J.T. and R.C. Gonzalez, Pattern recognition principles. 1974."},{"key":"e_1_3_2_1_11_1","volume-title":"Icml.","author":"Pelleg D.","year":"2000","unstructured":"Pelleg , D. and A.W. Moore . X-means: Extending k-means with efficient estimation of the number of clusters . in Icml. 2000 . Pelleg, D. and A.W. Moore. X-means: Extending k-means with efficient estimation of the number of clusters. in Icml. 2000."},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the 4th international conference on advances in pattern recognition and digital techniques.","author":"Ray S.","year":"1999","unstructured":"Ray , S. and R.H. Turi . Determination of number of clusters in k-means clustering and application in colour image segmentation . in Proceedings of the 4th international conference on advances in pattern recognition and digital techniques. 1999 . Calcutta, India. Ray, S. and R.H. Turi. Determination of number of clusters in k-means clustering and application in colour image segmentation. in Proceedings of the 4th international conference on advances in pattern recognition and digital techniques. 1999. Calcutta, India."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2003.1260785"},{"key":"e_1_3_2_1_14_1","volume-title":"Intelligent choice of the number of clusters in k-means clustering: an experimental study with different cluster spreads. Journal of classification","author":"Chiang M.M.","year":"2010","unstructured":"Chiang , M.M. - T. and B. Mirkin , Intelligent choice of the number of clusters in k-means clustering: an experimental study with different cluster spreads. Journal of classification , 2010 . 27(1): p. 3--40. Chiang, M.M.-T. and B. Mirkin, Intelligent choice of the number of clusters in k-means clustering: an experimental study with different cluster spreads. Journal of classification, 2010. 27(1): p. 3--40."},{"key":"e_1_3_2_1_15_1","first-page":"2006","volume":"201","author":"He Z.","unstructured":"He , Z. , et al. , Detecting the number of clusters in n-way probabilistic clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence , 201 0. 32(11): p. 2006 -- 2021 . He, Z., et al., Detecting the number of clusters in n-way probabilistic clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010. 32(11): p. 2006--2021.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_1_16_1","volume-title":"FCM-based model selection algorithms for determining the number of clusters. Pattern recognition","author":"Sun H.","year":"2004","unstructured":"Sun , H. , S. Wang , and Q. Jiang , FCM-based model selection algorithms for determining the number of clusters. Pattern recognition , 2004 . 37(10): p. 2027--2037. Sun, H., S. Wang, and Q. Jiang, FCM-based model selection algorithms for determining the number of clusters. Pattern recognition, 2004. 37(10): p. 2027--2037."},{"key":"e_1_3_2_1_17_1","volume-title":"2016 2nd IEEE International Conference on Computer and Communications (ICCC).","author":"Wang J.","year":"2016","unstructured":"Wang , J. , Y. Zhang , and X. Lan . Automatic cluster number selection by finding density peaks . in 2016 2nd IEEE International Conference on Computer and Communications (ICCC). 2016 . IEEE. Wang, J., Y. Zhang, and X. Lan. Automatic cluster number selection by finding density peaks. in 2016 2nd IEEE International Conference on Computer and Communications (ICCC). 2016. IEEE."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00293"},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of International Conference on Computational Intelligence.","author":"Ishioka T.","year":"2005","unstructured":"Ishioka , T. An expansion of X-means for automatically determining the optimal number of clusters . in Proceedings of International Conference on Computational Intelligence. 2005 . Ishioka, T. An expansion of X-means for automatically determining the optimal number of clusters. in Proceedings of International Conference on Computational Intelligence. 2005."},{"key":"e_1_3_2_1_20_1","volume-title":"International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization)","author":"Rani D.S.","unstructured":"Rani , D.S. and V. Shenbagamuthu , Modified K-Means Algorithm for Initial Centroid Detection . International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2 . Rani, D.S. and V. Shenbagamuthu, Modified K-Means Algorithm for Initial Centroid Detection. International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2."},{"key":"e_1_3_2_1_21_1","first-page":"71","volume":"200","author":"Memarsadeghi N.","unstructured":"Memarsadeghi , N. , et al. , A fast implementation of the ISODATA clustering algorithm. International Journal of Computational Geometry & Applications , 200 7. 17(01): p. 71 -- 103 . Memarsadeghi, N., et al., A fast implementation of the ISODATA clustering algorithm. International Journal of Computational Geometry & Applications, 2007. 17(01): p. 71--103.","journal-title":"A fast implementation of the ISODATA clustering algorithm. International Journal of Computational Geometry & Applications"},{"key":"e_1_3_2_1_22_1","volume-title":"A limited-iteration bisecting k-means for fast clustering large datasets. in 2016 IEEE Trustcom\/BigDataSE\/ISPA","author":"Zhuang Y.","year":"2016","unstructured":"Zhuang , Y. , Y. Mao , and X. Chen . A limited-iteration bisecting k-means for fast clustering large datasets. in 2016 IEEE Trustcom\/BigDataSE\/ISPA . 2016 . IEEE. Zhuang, Y., Y. Mao, and X. Chen. A limited-iteration bisecting k-means for fast clustering large datasets. in 2016 IEEE Trustcom\/BigDataSE\/ISPA. 2016. IEEE."},{"key":"e_1_3_2_1_23_1","unstructured":"Shim Y. J. Chung and I.-C. Choi. A comparison study of cluster validity indices using a nonhierarchical clustering algorithm. in International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and Internet Commerce (CIMCA-IAWTIC'06). 2005. IEEE.  Shim Y. J. Chung and I.-C. Choi. A comparison study of cluster validity indices using a nonhierarchical clustering algorithm. in International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and Internet Commerce (CIMCA-IAWTIC'06). 2005. IEEE."},{"key":"e_1_3_2_1_24_1","first-page":"159","volume":"198","author":"Milligan G.W.","unstructured":"Milligan , G.W. and M.C. Cooper , An examination of procedures for determining the number of clusters in a data set. Psychometrika , 198 5. 50(2): p. 159 -- 179 . Milligan, G.W. and M.C. Cooper, An examination of procedures for determining the number of clusters in a data set. Psychometrika, 1985. 50(2): p. 159--179.","journal-title":"Psychometrika"},{"key":"e_1_3_2_1_25_1","first-page":"187","volume":"198","author":"Milligan G.W.","unstructured":"Milligan , G.W. , A Monte Carlo study of thirty internal criterion measures for cluster analysis. Psychometrika , 198 1. 46(2): p. 187 -- 199 . Milligan, G.W., A Monte Carlo study of thirty internal criterion measures for cluster analysis. Psychometrika, 1981. 46(2): p. 187--199.","journal-title":"Psychometrika"},{"key":"e_1_3_2_1_26_1","first-page":"137","volume":"200","author":"Dimitriadou E.","unstructured":"Dimitriadou , E. , S. Dolni\u010dar , and A. Weingessel , An examination of indexes for determining the number of clusters in binary data sets. Psychometrika , 200 2. 67(1): p. 137 -- 159 . Dimitriadou, E., S. Dolni\u010dar, and A. Weingessel, An examination of indexes for determining the number of clusters in binary data sets. Psychometrika, 2002. 67(1): p. 137--159.","journal-title":"Psychometrika"},{"key":"e_1_3_2_1_27_1","volume-title":"An examination of the effect of six types of error perturbation on fifteen clustering algorithms. psychometrika","author":"Milligan G.W.","year":"1980","unstructured":"Milligan , G.W. , An examination of the effect of six types of error perturbation on fifteen clustering algorithms. psychometrika , 1980 . 45(3): p. 325--342. Milligan, G.W., An examination of the effect of six types of error perturbation on fifteen clustering algorithms. psychometrika, 1980. 45(3): p. 325--342."},{"key":"e_1_3_2_1_28_1","first-page":"1","volume":"197","author":"Cali\u0144ski T.","unstructured":"Cali\u0144ski , T. and J. Harabasz , A dendrite method for cluster analysis. Communications in Statistics-theory and Methods , 197 4. 3(1): p. 1 -- 27 . Cali\u0144ski, T. and J. Harabasz, A dendrite method for cluster analysis. 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Pattern Recognition, 2007. 40(3): p. 784--795.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/PST47121.2019.8949070"}],"event":{"name":"ICCDA 2020: 2020 The 4th International Conference on Compute and Data Analysis","location":"Silicon Valley CA USA","acronym":"ICCDA 2020"},"container-title":["Proceedings of the 2020 4th International Conference on Compute and Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3388142.3388164","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3388142.3388164","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:18Z","timestamp":1750197678000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3388142.3388164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,9]]},"references-count":32,"alternative-id":["10.1145\/3388142.3388164","10.1145\/3388142"],"URL":"https:\/\/doi.org\/10.1145\/3388142.3388164","relation":{},"subject":[],"published":{"date-parts":[[2020,3,9]]},"assertion":[{"value":"2020-04-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}