{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T04:17:32Z","timestamp":1778559452127,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,3,18]],"date-time":"2016-03-18T00:00:00Z","timestamp":1458259200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University of Saudi Arabia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000736","name":"University of East Anglia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000736","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2017,1]]},"DOI":"10.1007\/s10115-016-0930-3","type":"journal-article","created":{"date-parts":[[2016,3,18]],"date-time":"2016-03-18T20:08:44Z","timestamp":1458331724000},"page":"27-52","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4833-1602","authenticated-orcid":false,"given":"Aalaa","family":"Mojahed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beatriz","family":"de la Iglesia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,3,18]]},"reference":[{"key":"930_CR1","volume-title":"Data fusion in robotics and machine intelligence","author":"MA Abidi","year":"1992","unstructured":"Abidi MA, Gonzalez RC (1992) Data fusion in robotics and machine intelligence. Academic Press Professional Inc, San Diego"},{"key":"930_CR2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.chemolab.2013.06.006","volume":"129","author":"E Acar","year":"2013","unstructured":"Acar E, Rasmussen MA, Savorani F, Naes T, Bro R (2013) Understanding data fusion within the framework of coupled matrix and tensor factorizations. Chemom Intell Lab Syst 129:53\u201363 Multiway and Multiset Methods","journal-title":"Chemom Intell Lab Syst"},{"issue":"5","key":"930_CR3","first-page":"69","volume":"10","author":"OA Akeem","year":"2012","unstructured":"Akeem OA, Ogunyinka TK, Abimbola BL (2012) A framework for multimedia data mining in information technology environment. Int J Comput Sci Inf Secur (IJCSIS) 10(5):69\u201377","journal-title":"Int J Comput Sci Inf Secur (IJCSIS)"},{"key":"930_CR4","volume-title":"Modern information retrieval","author":"RA Baeza-Yates","year":"1999","unstructured":"Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley Longman Publishing Co. Inc., Boston"},{"key":"930_CR5","first-page":"229","volume-title":"Advances in knowledge discovery and data mining","author":"DJ Berndt","year":"1996","unstructured":"Berndt DJ, Clifford J (1996) Finding patterns in time series: a dynamic programming approach. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. American Association for Artificial Intelligence, Menlo Park, pp 229\u2013248"},{"key":"930_CR6","doi-asserted-by":"crossref","unstructured":"Bettencourt-Silva J, Iglesia B, Donell S, and Rayward-Smith V (2011) On creating a patient-centric database from multiple hospital information systems in a national health service secondary care setting. Methods Inf Med, 51(3):6730\u20136737","DOI":"10.3414\/ME10-01-0069"},{"key":"930_CR7","doi-asserted-by":"crossref","unstructured":"Bie TD, Tranchevent L-C, van Oeffelen LMM, Moreau Y (2007) Kernel-based data fusion for gene prioritization. In: ISMB\/ECCB (Supplement of Bioinformatics), pp 125\u2013132","DOI":"10.1093\/bioinformatics\/btm187"},{"key":"930_CR8","unstructured":"Bostr\u00f6m H, Andler SF, Brohede M, Johansson R, Karlsson A, van Laere J, Niklasson L, Nilsson M, Persson A, Ziemke T (2007) On the definition of information fusion as a field of research. Technical report, Institutionen f\u00f6r kommunikation och information"},{"issue":"5","key":"930_CR9","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/S0090-4295(00)00753-6","volume":"56","author":"TY Chan","year":"2000","unstructured":"Chan TY, Partin AW, Walsh PC, Epstein JI (2000) Prognostic significance of Gleason score 3+4 versus Gleason score 4+3 tumor at radical prostatectomy. Urology 56(5):823\u2013827","journal-title":"Urology"},{"issue":"1","key":"930_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S1566-2535(02)00122-7","volume":"4","author":"BV Dasarathy","year":"2003","unstructured":"Dasarathy BV (2003) Information fusion, data mining, and knowledge discovery. Inf Fusion 4(1):1\u20132","journal-title":"Inf Fusion"},{"key":"930_CR11","doi-asserted-by":"crossref","unstructured":"Dhillon IS (2001) Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, KDD \u201901, pp 269\u2013274, New York, NY, USA. ACM","DOI":"10.1145\/502512.502550"},{"key":"930_CR12","doi-asserted-by":"crossref","unstructured":"Dhillon IS, Mallela S, Modha D (2003) Information-theoretic co-clustering. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, pp 89\u201398","DOI":"10.1145\/956750.956764"},{"key":"930_CR13","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297\u2013302","journal-title":"Ecology"},{"key":"930_CR14","doi-asserted-by":"crossref","unstructured":"Dimitriadou E, Weingessel A, Hornik K (2002) A combination scheme for fuzzy clustering. In: Pal N, Sugeno M (eds) Advances in soft computing (AFSS 2002), vol 2275., Lecture notes in computer science, Berlin Heidelberg, Springer, pp 332\u2013338","DOI":"10.1007\/3-540-45631-7_44"},{"issue":"1","key":"930_CR15","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.inffus.2010.06.001","volume":"12","author":"N-EE Faouzi","year":"2011","unstructured":"Faouzi N-EE, Leung H, Kurian A (2011) Data fusion in intelligent transportation systems: progress and challenges a survey. Inf Fusion 12(1):4\u201310 Special Issue on Intelligent Transportation Systems","journal-title":"Inf Fusion"},{"key":"930_CR16","doi-asserted-by":"crossref","unstructured":"Gao B, Liu T, Zheng X, Cheng Q, Ma W (2006) Consistent bipartite graph co-partitioning for star structured high-order heterogeneous data co-clustering. In: Proceedings of the 6th IEEE international conference on data mining (ICDM), pp 1\u201331","DOI":"10.1109\/ICDM.2006.154"},{"key":"930_CR17","unstructured":"Google (2015) Explore trends. http:\/\/www.google.com\/trends\/?hl=en-GB . Accessed 04 April 2015"},{"key":"930_CR18","doi-asserted-by":"crossref","unstructured":"Greene P, Cunningham P (2009) A matrix factorization approach for integrating multiple data views. In: Proceedings of the European conference on machine learning and knowledge discovery in databases: part I, pp 423\u2013438","DOI":"10.1007\/978-3-642-04180-8_45"},{"issue":"1","key":"930_CR19","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/5.554205","volume":"85","author":"D Hall","year":"1997","unstructured":"Hall D, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6\u201323","journal-title":"Proc IEEE"},{"key":"930_CR20","doi-asserted-by":"crossref","unstructured":"Hays J, Efros AA (2007) Scene completion using millions of photographs. In: ACM SIGGRAPH, (2007) papers, SIGGRAPH \u201907, New York, NY, USA. ACM","DOI":"10.1145\/1275808.1276382"},{"key":"930_CR21","unstructured":"Huang A (2008) Similarity measures for text document clustering. In: Holland J, Nicholas A, Brignoli D (eds) New Zealand computer science research student conference, pp 49\u201356"},{"key":"930_CR22","unstructured":"Inc. F (2015) The world\u2019s most powerful celebrities. http:\/\/www.forbes.com\/ . Accessed 24 April 2015"},{"key":"930_CR23","first-page":"223","volume":"44","author":"S Jaccard","year":"1908","unstructured":"Jaccard S (1908) Nouvelles researches sur la distribution florale. Bull Soc Vaud Sci Nat 44:223\u2013270","journal-title":"Bull Soc Vaud Sci Nat"},{"key":"930_CR24","first-page":"405","volume-title":"Statistical data analysis based on the L1-norm and related methods","author":"L Kaufman","year":"1987","unstructured":"Kaufman L, Rousseeuw PJ (1987) Clustering by means of medoids. In: Dodge Y (ed) Statistical data analysis based on the L1-norm and related methods. Springer, Berlin Heidelberg, pp 405\u2013416"},{"key":"930_CR25","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316801","volume-title":"Finding groups in data, an introduction to cluster analysis","author":"L Kaufman","year":"1990","unstructured":"Kaufman L, Rousseeuw PJ (1990) Finding groups in data, an introduction to cluster analysis. Wiley, New York"},{"issue":"1","key":"930_CR26","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2011.08.001","volume":"14","author":"B Khaleghi","year":"2013","unstructured":"Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multisensor data fusion: a review of the state-of-the-art. Inf Fusion 14(1):28\u201344","journal-title":"Inf Fusion"},{"issue":"16","key":"930_CR27","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1093\/bioinformatics\/bth294","volume":"20","author":"GRG Lanckriet","year":"2004","unstructured":"Lanckriet GRG, Bie TD, Cristianini N, Jordan MI, Noble WS (2004a) A statistical framework for genomic data fusion. Bioinformatics 20(16):2626\u20132635","journal-title":"Bioinformatics"},{"key":"930_CR28","first-page":"27","volume":"5","author":"GRG Lanckriet","year":"2004","unstructured":"Lanckriet GRG, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI (2004b) Learning the kernel matrix with semidefinite programming. J Mach Learn Res 5:27\u201372","journal-title":"J Mach Learn Res"},{"key":"930_CR29","unstructured":"Laney D (2001) 3D data management: controlling data volume, velocity, and variety. Technical report, META Group"},{"key":"930_CR30","doi-asserted-by":"crossref","unstructured":"Larsen B, Aone C (1999) Fast and effective text mining using linear-time document clustering. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, KDD \u201999, pp 16\u201322, New York, NY, USA. ACM","DOI":"10.1145\/312129.312186"},{"key":"930_CR31","doi-asserted-by":"crossref","unstructured":"Li X, Wu C, Zach C, Lazebnik S, Frahm J-M (2008) Modeling and recognition of landmark image collections using iconic scene graphs. In: Proceedings of the 10th European conference on computer vision: part I, ECCV \u201908, pp 427\u2013440, Springer-Verlag, Berlin, Heidelberg","DOI":"10.1007\/978-3-540-88682-2_33"},{"key":"930_CR32","doi-asserted-by":"crossref","unstructured":"Liang P, Klein D (2009) Online EM for unsupervised models. In: Proceedings of human language technologies: the 2009 annual conference of the North American chapter of the Association for Computational Linguistics, NAACL \u201909, pp 611\u2013619, Stroudsburg, PA, USA","DOI":"10.3115\/1620754.1620843"},{"key":"930_CR33","doi-asserted-by":"crossref","unstructured":"Long B, Zhang Z, Wu X, Yu PS (2006) Spectral clustering for multi-type relational data. In: ICML, pp 585\u2013592","DOI":"10.1145\/1143844.1143918"},{"key":"930_CR34","doi-asserted-by":"crossref","unstructured":"Ma H, Yang H, Lyu MR, King I (2008) Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on information and knowledge management, CIKM \u201908, pp 931\u2013940, New York, NY, USA. ACM","DOI":"10.1145\/1458082.1458205"},{"issue":"11","key":"930_CR35","first-page":"165","volume":"10","author":"TN Manjunath","year":"2010","unstructured":"Manjunath TN, Hegadi RS, Ravikumar GK (2010) A survey on multimedia data mining and its relevance today. Int J Comput Sci Netw Secur (IJCSNS) 10(11):165\u2013170","journal-title":"Int J Comput Sci Netw Secur (IJCSNS)"},{"key":"930_CR36","doi-asserted-by":"crossref","unstructured":"Maragos P, Gros P, Katsamanis A, Papandreou G (2008) Cross-modal integration for performance improving in multimedia: a review. In: Maragos P, Potamianos A, Gros P (eds) Multimodal processing and interaction, vol 33., multimedia systems and applications, US, Springer, pp 1\u201346","DOI":"10.1007\/978-0-387-76316-3_1"},{"key":"930_CR37","unstructured":"Mojahed A (2015) Heterogeneous data: data mining solutions. http:\/\/amojahed.wix.com\/heterogeneous-data . Accessed 30 Aug 2015"},{"key":"930_CR38","doi-asserted-by":"crossref","unstructured":"Mojahed A, Bettencourt-Silva J, Wang W, de la Iglesia B (2015) Applying clustering analysis to heterogeneous data using similarity matrix fusion (smf). In: Perner P (ed) Machine learning and data mining in pattern recognition, vol 9166 of lecture notes in computer science, pp 251\u2013265. Springer International Publishing","DOI":"10.1007\/978-3-319-21024-7_17"},{"key":"930_CR39","doi-asserted-by":"crossref","unstructured":"Mojahed A, De La Iglesia B (2014) A fusion approach to computing distance for heterogeneous data. In: Proceedings of the sixth international conference on knowledge discover and information retrieval (KDIR 2014), pp 269\u2013276, Rome, Italy. SCITEPRESS","DOI":"10.5220\/0005083702690276"},{"key":"930_CR40","unstructured":"Ng R, Han J (1994) Efficient and effective clustering methods for spatial data mining. In: Proceedings of the 20th conference on VLDB, pp 144\u2013155"},{"key":"930_CR41","unstructured":"NICE (2014) Prostate cancer: diagnosis and treatment. NICE Clin Guidel 175:1\u201348"},{"issue":"3","key":"930_CR42","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1023\/A:1011139631724","volume":"42","author":"A Oliva","year":"2001","unstructured":"Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145\u2013175","journal-title":"Int J Comput Vis"},{"issue":"2","key":"930_CR43","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1016\/j.eswa.2008.01.039","volume":"36","author":"H-S Park","year":"2009","unstructured":"Park H-S, Jun C-H (2009) A simple and fast algorithm for k-medoids clustering. Expert Syst Appl 36(2):3336\u20133341","journal-title":"Expert Syst Appl"},{"issue":"2","key":"930_CR44","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1089\/10665270252935539","volume":"9","author":"P Pavlidis","year":"2002","unstructured":"Pavlidis P, Cai J, Weston J, Noble WS (2002) Learning gene functional classifications from multiple data types. J Comput Biol 9(2):401\u2013411","journal-title":"J Comput Biol"},{"issue":"336","key":"930_CR45","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1958","unstructured":"Rand WM (1958) Objective criteria foe the evaluation of clustering methods. J Am Stat Assoc 66(336):846\u2013850","journal-title":"J Am Stat Assoc"},{"key":"930_CR46","doi-asserted-by":"crossref","unstructured":"Ratanamahatana CA, Keogh E (2005) Three myths about dynamic time warping data mining. In: Proceedings of SIAM international conference on data mining (SDM05), pp 506\u2013510","DOI":"10.1137\/1.9781611972757.50"},{"key":"930_CR47","unstructured":"Reuters T (2015a) ISI Web of Knowledge: Journal citation reports. http:\/\/wokinfo.com\/products_tools\/analytical\/jcr\/ . Accessed 14 April 2015"},{"key":"930_CR48","unstructured":"Reuters T (2015b) Web of Science. http:\/\/apps.webofknowledge.com\/WOS_GeneralSearch_input.do?product=WOS&SID=P1JvWUMqY5wYpc8EIER&search_mode=GeneralSearch . Accessed 14 April 2015"},{"key":"930_CR49","volume-title":"Introduction to modern information retrieval","author":"G Salton","year":"1987","unstructured":"Salton G, McGill MJ (1987) Introduction to modern information retrieval. McGraw-Hill, New York"},{"key":"930_CR50","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1186\/1471-2105-11-309","volume":"11","author":"Y Shi","year":"2010","unstructured":"Shi Y, Falck T, Daemen A, Tranchevent L-C, Suykens JAK, De Moor B, Moreau Y (2010) L2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinform 11:309\u2013332","journal-title":"BMC Bioinform"},{"key":"930_CR51","unstructured":"Society TRH (2014) Plants. https:\/\/www.rhs.org.uk\/"},{"key":"930_CR52","first-page":"583","volume":"3","author":"A Strehl","year":"2003","unstructured":"Strehl A, Ghosh J (2003) Cluster ensembles: a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583\u2013617","journal-title":"J Mach Learn Res"},{"key":"930_CR53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4161\/sysb.28527","volume":"2","author":"M \u017ditnik","year":"2014","unstructured":"\u017ditnik M, Zupan B (2014) Matrix factorization-based data fusion for gene function prediction in baker\u2019s yeast and slime mold. Syst Biomed 2:1\u20137","journal-title":"Syst Biomed"},{"issue":"7","key":"930_CR54","doi-asserted-by":"crossref","first-page":"e40358","DOI":"10.1371\/journal.pone.0040358","volume":"7","author":"MH Vliet van","year":"2012","unstructured":"van Vliet MH, Horlings HM, van de Vijver MJ, Reinders MJT, Wessels LFA (2012) Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome. PLoS One 7(7):e40358","journal-title":"PLoS One"},{"key":"930_CR55","doi-asserted-by":"crossref","unstructured":"Wang J, Zeng H, Chen Z, Lu H, Tao L, Ma W (2003) Recom: reinforcement clustering of multi-type interrelated data objects. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, pp 274\u2013281","DOI":"10.1145\/860435.860486"},{"key":"930_CR56","unstructured":"Wikipedia (2015) Wikipedia: the free encyclopedia. https:\/\/en.wikipedia.org\/wiki\/Main_Page . Accessed 24 April 2015"},{"key":"930_CR57","unstructured":"Yu S, Moor B, Moreau Y (2009) Clustering by heterogeneous data fusion: framework and applications. In: NIPS workshop"},{"key":"930_CR58","unstructured":"Zeng H, Chen Z, Ma W (2002) a unified framework for clustering heterogeneous web objects. In: Proceedings of the 3rd international conference on web information systems engineering (WISE), pp 161\u2013172"},{"key":"930_CR59","doi-asserted-by":"crossref","unstructured":"Zha H, Ding C, Gu M (2001) Bipartite graph partitioning and data clustering. In: Proceedings of the 10th international conference on information and knowledge management, pp 25\u201332","DOI":"10.1145\/502585.502591"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-016-0930-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-016-0930-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-016-0930-3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-016-0930-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T03:36:52Z","timestamp":1718422612000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-016-0930-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,18]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"alternative-id":["930"],"URL":"https:\/\/doi.org\/10.1007\/s10115-016-0930-3","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,3,18]]}}}