{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:29:15Z","timestamp":1762324155196,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2017,10,5]],"date-time":"2017-10-05T00:00:00Z","timestamp":1507161600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad (ES)","doi-asserted-by":"crossref","award":["TIN2014-55894-C2-1-R"],"award-info":[{"award-number":["TIN2014-55894-C2-1-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Prog Artif Intell"],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1007\/s13748-017-0135-3","type":"journal-article","created":{"date-parts":[[2017,10,5]],"date-time":"2017-10-05T03:54:41Z","timestamp":1507175681000},"page":"81-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An approach to validity indices for clustering techniques in Big Data"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3397-4704","authenticated-orcid":false,"given":"Jos\u00e9 Mar\u00eda","family":"Luna-Romera","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge","family":"Garc\u00eda-Guti\u00e9rrez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda","family":"Mart\u00ednez-Ballesteros","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 C.","family":"Riquelme Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,5]]},"reference":[{"key":"135_CR1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jhydrol.2017.04.047","volume":"550","author":"A Abdi","year":"2017","unstructured":"Abdi, A., Hassanzadeh, Y., Ouarda, T.: Regional frequency analysis using Growing Neural Gas network. J. Hydrol. 550, 92\u2013102 (2017)","journal-title":"J. Hydrol."},{"issue":"2","key":"135_CR2","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s13042-015-0335-8","volume":"8","author":"A Alok","year":"2017","unstructured":"Alok, A., Saha, S., Ekbal, A.: Semi-supervised clustering for gene-expression data in multiobjective optimization framework. Int. J. Mach. Learn. Cybern. 8(2), 421\u2013439 (2017)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"135_CR3","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.patcog.2016.10.017","volume":"63","author":"V Berikov","year":"2017","unstructured":"Berikov, V., Pestunov, I.: Ensemble clustering based on weighted co-association matrices: error bound and convergence properties. Pattern Recognit. 63, 427\u2013436 (2017)","journal-title":"Pattern Recognit."},{"key":"135_CR4","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.jclepro.2016.09.201","volume":"153","author":"C Boone","year":"2017","unstructured":"Boone, C., Skipper, J., Hazen, B.: A framework for investigating the role of big data in service parts management. J. Clean. Prod. 153, 687\u2013691 (2017)","journal-title":"J. Clean. Prod."},{"issue":"1","key":"135_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","volume":"3","author":"T Calinski","year":"1974","unstructured":"Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1\u201327 (1974)","journal-title":"Commun. Stat. Theory Methods"},{"issue":"3","key":"135_CR6","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/TPAMI.2010.88","volume":"33","author":"W-Y Chen","year":"2011","unstructured":"Chen, W.-Y., Song, Y., Bai, H., Lin, C.-J., Chang, E.Y.: Parallel Spectral Clustering in Distributed Systems. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 568\u2013586 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"135_CR7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40537-017-0070-y","volume":"4","author":"H Daki","year":"2017","unstructured":"Daki, H., El Hannani, A., Aqqal, A., Haidine, A., Dahbi, A.: Big Data management in smart grid: concepts, requirements and implementation. J. Big Data 4(1), 13 (2017)","journal-title":"J. Big Data"},{"issue":"2","key":"135_CR8","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"PAMI\u20131","author":"D Davies","year":"1979","unstructured":"Davies, D., Bouldin, D.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI\u20131(2), 224\u2013227 (1979)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"135_CR9","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/1327452.1327492","volume":"51","author":"J Dean","year":"2008","unstructured":"Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107\u2013113 (2008)","journal-title":"Commun. ACM"},{"issue":"4","key":"135_CR10","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/0031-3203(76)90045-5","volume":"8","author":"R Dubes","year":"1976","unstructured":"Dubes, R., Jain, A.K.: Clustering techniques: the user\u2019s dilemma. Pattern Recognit. 8(4), 247\u2013260 (1976)","journal-title":"Pattern Recognit."},{"issue":"1","key":"135_CR11","doi-asserted-by":"crossref","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":"3","key":"135_CR12","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TETC.2014.2330519","volume":"2","author":"A Fahad","year":"2014","unstructured":"Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A\u00a0.Y., Foufou, S., Bouras, A.: A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans. Emerg. Top. Comput. 2(3), 267\u2013279 (2014)","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"135_CR13","first-page":"2017","volume":"1","author":"L Gallos","year":"2017","unstructured":"Gallos, L., Korczy\u0144ski, M., Fefferman, N.: Anomaly detection through information sharing under different topologies. Eurasip J. Inf. Secur. 1, 2017 (2017)","journal-title":"Eurasip J. Inf. Secur."},{"key":"135_CR14","doi-asserted-by":"crossref","unstructured":"Ghemawat, S., Gobioff, H., Leung, S.-T.: The Google File System, vol.\u00a037, pp. 29\u201343. ACM Press, New York, USA (2003) (cited By 2613)","DOI":"10.1145\/1165389.945450"},{"key":"135_CR15","doi-asserted-by":"crossref","unstructured":"Han, J., Kamber, M., Pei, J.: Cluster analysis: basic concepts and methods. In: Data Mining: Concepts and Techniques, pp. 443\u2013495. Elsevier, USA (2012)","DOI":"10.1016\/B978-0-12-381479-1.00010-1"},{"issue":"3","key":"135_CR16","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1111\/j.1467-9876.2012.01066.x","volume":"62","author":"C Hennig","year":"2013","unstructured":"Hennig, C., Liao, T.: How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification. J. R. Stat. Soc. Ser. C Appl. Stat. 62(3), 309\u2013369 (2013)","journal-title":"J. R. Stat. Soc. Ser. C Appl. Stat."},{"key":"135_CR17","doi-asserted-by":"crossref","unstructured":"Holmes, G., Donkin, A., Witten, I.: WEKA: a machine learning workbench. In: Proceedings of ANZIIS \u201994\u2014Australian New Zealnd Intelligent Information Systems Conference, Number JANUARY 1994, pp. 357\u2013361. (1994)","DOI":"10.1109\/ANZIIS.1994.396988"},{"issue":"3","key":"135_CR18","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s11634-013-0158-y","volume":"8","author":"J Jacques","year":"2014","unstructured":"Jacques, J., Preda, C.: Functional data clustering: a survey. Adv. Data Anal. Classif. 8(3), 231\u2013255 (2014)","journal-title":"Adv. Data Anal. Classif."},{"issue":"8","key":"135_CR19","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"A\u00a0K Jain","year":"2010","unstructured":"Jain, A\u00a0.K.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31(8), 651\u2013666 (2010)","journal-title":"Pattern Recognit. Lett."},{"issue":"6","key":"135_CR20","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1007\/s00521-016-2570-7","volume":"28","author":"R\u00a0B Jerome","year":"2017","unstructured":"Jerome, R\u00a0.B., \u00e4t\u00f6nen, K\u00a0.H.: Anomaly detection and classification using a metric for determining the significance of failures. Neural Comput. Appl. 28(6), 1265\u20131275 (2017)","journal-title":"Neural Comput. Appl."},{"key":"135_CR21","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.asoc.2017.04.031","volume":"57","author":"C Jinyin","year":"2017","unstructured":"Jinyin, C., Xiang, L., Haibing, Z., Xintong, B.: A novel cluster center fast determination clustering algorithm. Appl. Soft Comput. 57, 539\u2013555 (2017)","journal-title":"Appl. Soft Comput."},{"issue":"2","key":"135_CR22","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1007\/s10586-017-0763-1","volume":"20","author":"J Kim","year":"2017","unstructured":"Kim, J., Lee, W., Song, J\u00a0.J., Lee, S.-B.: Optimized combinatorial clustering for stochastic processes. Clust. Comput. 20(2), 1135\u20131148 (2017)","journal-title":"Clust. Comput."},{"key":"135_CR23","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.ins.2017.02.010","volume":"393","author":"E Lord","year":"2017","unstructured":"Lord, E., Willems, M., Lapointe, F.-J., Makarenkov, V.: Using the stability of objects to determine the number of clusters in datasets. Inf. Sci. 393, 29\u201346 (2017)","journal-title":"Inf. Sci."},{"key":"135_CR24","unstructured":"Luna-Romera, J.M.: Clustering Synthetic Big Datasets Generator. https:\/\/github.com\/josemarialuna\/CreateRandomDataset (2017). Accessed 20 July 2017"},{"issue":"4","key":"135_CR25","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s12530-015-9136-2","volume":"7","author":"A Mazinan","year":"2016","unstructured":"Mazinan, A.: On cluster validity indices with its application to interleaved radar pulse separation through fuzzy-based representation. Evol. Syst. 7(4), 243\u2013254 (2016)","journal-title":"Evol. Syst."},{"key":"135_CR26","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ins.2013.11.016","volume":"260","author":"Z Miller","year":"2014","unstructured":"Miller, Z., Dickinson, B., Deitrick, W., Hu, W., Wang, A.H.: Twitter spammer detection using data stream clustering. Inf. Sci. 260, 64\u201373 (2014)","journal-title":"Inf. Sci."},{"key":"135_CR27","doi-asserted-by":"crossref","unstructured":"Mohammed, A.J., Yusof, Y., Husni, H.: Fireflyclust: an automated hierarchical text clustering approach. Jurnal Teknologi, 79(5), 11\u201322 (2017)","DOI":"10.11113\/jt.v79.5408"},{"key":"135_CR28","unstructured":"Parejo, J.A., Garcia, J., Ruiz-Cortes, A., Riquelme, J.C.: Statservice: Herramienta de an\u00e1lisis estadistico como soportepara la investigacion con metaheuristicas. In: Actas del VIII Congreso Expa\u00f1ol sobre Metaheur\u00edsticas, Algoritmos Evolutivos y Bio-inspirados. Albacete, Espa\u00f1a (2012)"},{"key":"135_CR29","doi-asserted-by":"crossref","unstructured":"Perez-Chacon, R., Talavera-Llames, R., Martinez-Alvarez, F., Troncoso A.: Finding Electric Energy Consumption Patterns in Big Time Series Data. In: Omatu, S., et al. (eds.) Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol. 474, pp. 231\u2013238. Springer, Cham (2016)","DOI":"10.1007\/978-3-319-40162-1_25"},{"issue":"C","key":"135_CR30","doi-asserted-by":"crossref","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(C), 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"key":"135_CR31","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.marpol.2017.05.032","volume":"83","author":"A\u00a0G Rumson","year":"2017","unstructured":"Rumson, A\u00a0.G., Hallett, S\u00a0.H., Brewer, T\u00a0.R.: Coastal risk adaptation: the potential role of accessible geospatial Big Data. Mar. Policy 83, 100\u2013110, (2017)","journal-title":"Mar. Policy"},{"key":"135_CR32","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.is.2016.12.003","volume":"65","author":"T Sagi","year":"2017","unstructured":"Sagi, T., Gal, A., Barkol, O., Bergman, R., Avram, A.: Multi-source uncertain entity resolution: transforming holocaust victim reports into people. Inf. Syst. 65, 124\u2013136 (2017)","journal-title":"Inf. Syst."},{"key":"135_CR33","doi-asserted-by":"crossref","unstructured":"Sevilla-Villanueva, B., Gibert, K., \u00e0nchez-Marr\u00e8, M.S.: Using CVI for Understanding Class Topology in Unsupervised Scenarios, pp. 135\u2013149. Springer, Cham (2016)","DOI":"10.1007\/978-3-319-44636-3_13"},{"key":"135_CR34","unstructured":"Spark, A.: Apache Spark, Lightning-Fast Cluster Computing. https:\/\/spark.apache.org\/ (2017). Accessed 20 June 2017"},{"key":"135_CR35","unstructured":"Spark, A.: MLlib is Apache Spark\u2019s Scalable Machine Learning Library. https:\/\/spark.apache.org\/mllib\/ (2017). Accessed 20 June 2017"},{"key":"135_CR36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2017.01.016","volume":"89","author":"Q Tong","year":"2017","unstructured":"Tong, Q., Li, X., Yuan, B.: A highly scalable clustering scheme using boundary information. Pattern Recognit. Lett. 89, 1\u20137 (2017)","journal-title":"Pattern Recognit. Lett."},{"issue":"3","key":"135_CR37","first-page":"1037","volume":"13","author":"M Yang","year":"2017","unstructured":"Yang, M., Mei, H., Huang, D.: An effective detection of satellite images via k-means clustering on hadoop system. Int. J. Innov. Comput. Inf. Control 13(3), 1037\u20131046 (2017)","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"135_CR38","unstructured":"Zaharia, M.. Chowdhury, M., Das, T., Dave, A., Ma, J., McCauly, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Presented as Part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12), pp. 15\u201328, San Jose, CA, USENIX (2012)"},{"key":"135_CR39","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.inffus.2017.04.002","volume":"39","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Yang, L.T., Chen, Z., Li, P.: High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT. Inf. Fusion 39, 72\u201380 (2018)","journal-title":"Inf. Fusion"},{"key":"135_CR40","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.meegid.2017.03.028","volume":"51","author":"R Zhang","year":"2017","unstructured":"Zhang, R., Xu, C., Duan, Z.: Novel antigenic shift in HA sequences of H1N1 viruses detected by big data analysis. Infect. Genet. Evol. 51, 138\u2013142 (2017)","journal-title":"Infect. Genet. Evol."}],"container-title":["Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13748-017-0135-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-017-0135-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-017-0135-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T04:15:21Z","timestamp":1570162521000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13748-017-0135-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,5]]},"references-count":40,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["135"],"URL":"https:\/\/doi.org\/10.1007\/s13748-017-0135-3","relation":{},"ISSN":["2192-6352","2192-6360"],"issn-type":[{"type":"print","value":"2192-6352"},{"type":"electronic","value":"2192-6360"}],"subject":[],"published":{"date-parts":[[2017,10,5]]}}}