{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:59:38Z","timestamp":1773889178792,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,10,19]],"date-time":"2019-10-19T00:00:00Z","timestamp":1571443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,10,19]],"date-time":"2019-10-19T00:00:00Z","timestamp":1571443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2018GZ0103"],"award-info":[{"award-number":["2018GZ0103"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Due to cluster instability, not in the cluster monitoring system. This paper focuses on the missing data imputation processing for the cluster monitoring application and proposes a new hybrid multiple imputation framework. This new imputation approach is different from the conventional multiple imputation technologies in the fact that it attempts to impute the missing data for an arbitrary missing pattern with a model-based and data-driven combination architecture. Essentially, the deep neural network, as the data model, extracts deep features from the data and deep features are further calculated then by a regression or data-driven strategies and used to create the estimation of missing data with the arbitrary missing pattern. This paper gives evidence that if we can train a deep neural network to construct the deep features of the data, imputation based on deep features is better than that directly on the original data. In the experiments, we compare the proposed method with other conventional multiple imputation approaches for varying missing data patterns, missing ratios, and different datasets including real cluster data. The result illustrates that when data encounters larger missing ratio and various missing patterns, the proposed algorithm has the ability to achieve more accurate and stable imputation performance.<\/jats:p>","DOI":"10.1007\/s10489-019-01560-y","type":"journal-article","created":{"date-parts":[[2019,10,19]],"date-time":"2019-10-19T16:14:56Z","timestamp":1571501696000},"page":"860-877","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Data-driven missing data imputation in cluster monitoring system based on deep neural network"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3476-7536","authenticated-orcid":false,"given":"Jie","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"NianHua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Ashraful","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,19]]},"reference":[{"issue":"7","key":"1560_CR1","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/j.parco.2004.04.001","volume":"30","author":"ML Massie","year":"2004","unstructured":"Massie ML, Chun BN, Culler DE (2004) The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput 30(7):817\u2013840","journal-title":"Parallel Comput"},{"key":"1560_CR2","doi-asserted-by":"crossref","unstructured":"Nikfalazar S, Yeh C, Bedingfield S, Khorshidi HA (2017) A new iterative fuzzy clustering algorithm for multiple imputation of missing data, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, pp 1\u20136","DOI":"10.1109\/FUZZ-IEEE.2017.8015560"},{"key":"1560_CR3","doi-asserted-by":"crossref","unstructured":"Jea K, Hsu C, Tang L (2018) A missing data imputation method with distance function. International Conference on Machine Learning and Cybernetics (ICMLC), pp 450\u2013455","DOI":"10.1109\/ICMLC.2018.8526985"},{"key":"1560_CR4","doi-asserted-by":"crossref","unstructured":"Mazzutti T, Roisenberg M, de Freitas Filho PJ (2018) Adaptive missing data imputation with incremental Neuro-Fuzzy gaussian mixture network (INFGMN), International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, pp 1\u20138","DOI":"10.1109\/IJCNN.2018.8489515"},{"issue":"2","key":"1560_CR5","first-page":"326","volume":"83","author":"P Berglund","year":"2014","unstructured":"Berglund P, Heeringa S (2014) Multiple imputation of missing data using SAS. Int Stat Rev 83(2):326\u2013327","journal-title":"Int Stat Rev"},{"key":"1560_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/9781119013563","volume-title":"Statistical Analysis with Missing Data","author":"Roderick J. A. Little","year":"2002","unstructured":"Little RJA, Donald BR (2002) Statistical Analysis with Missing Data, 2edn"},{"issue":"6","key":"1560_CR7","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11348","volume":"10","author":"Fei Tang","year":"2017","unstructured":"Tang F, Ishwaran H (2017) Random forest missing data algorithms. Stat Anal Data Min: The ASA Data Sci Journal, pp 363\u2013 377","journal-title":"Statistical Analysis and Data Mining: The ASA Data Science Journal"},{"key":"1560_CR8","doi-asserted-by":"crossref","unstructured":"Zhang Y, Liu Y (2009) Missing traffic flow data prediction using least squares support vector machines in urban arterial streets. In: IEEE Symposium on Computational Intelligence and Data Mining, pp 76\u201383","DOI":"10.1109\/CIDM.2009.4938632"},{"key":"1560_CR9","unstructured":"Suh MK, Woodbridge J, Lan M, Bui A, Evangelista LS, Sarrafzadeh M (2011) Missing data imputation for remote CHF patient monitoring systems. International Conference of the IEEE Engineering in Medicine Biology Society, pp 3184\u2013 3187"},{"key":"1560_CR10","doi-asserted-by":"crossref","unstructured":"Allison PD (2001) Missing data, vol 136. Sage Publications, Newbury Park","DOI":"10.4135\/9781412985079"},{"key":"1560_CR11","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.ecolind.2011.04.023","volume":"17","author":"T Srebotnjak","year":"2012","unstructured":"Srebotnjak T, Carr G, Sherbinin AD, Rickwood C (2012) A global Water Quality Index and hot-deck imputation of missing data. Ecol Indic 17:108\u2013119","journal-title":"Ecol Indic"},{"issue":"12","key":"1560_CR12","doi-asserted-by":"publisher","first-page":"31069","DOI":"10.3390\/s151229842","volume":"15","author":"CC Turrado","year":"2015","unstructured":"Turrado CC, Lasheras FS, Calvo-Rolle JL, Pi\u00f1on-Pazos A. J., Juez FJC (2015) A new missing data imputation algorithm applied to electrical data loggers. Sensors 15(12):31069\u201331082","journal-title":"Sensors"},{"key":"1560_CR13","doi-asserted-by":"crossref","unstructured":"Sessa J, Syed D (2016) Techniques to deal with missing data, 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), Ras Al Khaimah, pp 1\u20134","DOI":"10.1109\/ICEDSA.2016.7818486"},{"key":"1560_CR14","doi-asserted-by":"crossref","unstructured":"Krause RW, Huisman M, Steglich C, Sniiders TA (2018) Missing network data a comparison of different imputation methods, IEEE\/ACM international conference on advances in social networks analysis and mining (ASONAM), Barcelona, pp 159\u2013163","DOI":"10.1109\/ASONAM.2018.8508716"},{"key":"1560_CR15","unstructured":"Duan Y, Lv Y, Kang W, Zhao Y (2014) A deep learning based approach for traffic data imputation. In: 17th International IEEE Conference on Intelligent Transportation Systems, pp 912\u2013917"},{"key":"1560_CR16","unstructured":"Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2016) Recurrent Neural Networks for Multivariate Time Series with Missing Values. arXiv: http:\/\/arXiv.org\/abs\/1606.01865"},{"key":"1560_CR17","doi-asserted-by":"crossref","unstructured":"Thirukumaran S, Sumathi A (2016) Improving accuracy rate of imputation of missing data using classifier methods, 10th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, pp 1\u20137","DOI":"10.1109\/ISCO.2016.7726908"},{"key":"1560_CR18","doi-asserted-by":"crossref","unstructured":"Razavi-Far R, Saif M (2016) Imputation of missing data using fuzzy neighborhood density-based clustering. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, pp 1834\u20131841","DOI":"10.1109\/FUZZ-IEEE.2016.7737913"},{"key":"1560_CR19","doi-asserted-by":"crossref","unstructured":"Azim S, Aggarwal S (2016) Using fuzzy c means and multi layer perceptron for data imputation: Simple v\/s complex dataset. 3rd International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, pp 197\u2013202","DOI":"10.1109\/RAIT.2016.7507901"},{"key":"1560_CR20","doi-asserted-by":"crossref","unstructured":"Soni S, Sharma I (2017) An imputation-based method for fuzzy clustering of incomplete data,\u201d 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, pp 616\u2013621","DOI":"10.1109\/ICCSP.2017.8286431"},{"key":"1560_CR21","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/978-981-10-2471-9_17","volume":"507","author":"MB Myneni","year":"2017","unstructured":"Myneni MB, Srividya Y, Dandamudi A (2017) Correlated Cluster-Based imputation for treatment of missing values, inproceedings of the first international conference on computational intelligence and informatics. Adv Intell Syst Comput 507:171\u2013178","journal-title":"Adv Intell Syst Comput"},{"key":"1560_CR22","doi-asserted-by":"crossref","unstructured":"Susanti SP, Azizah FN (2017) Imputation of missing value using dynamic Bayesian network for multivariate time series data,International Conference on Data and Software Engineering (ICoDSE), Palembang, pp 1\u20135","DOI":"10.1109\/ICODSE.2017.8285864"},{"key":"1560_CR23","doi-asserted-by":"crossref","unstructured":"Chen X (2018) An Improved Self-Representation Approach for Missing Value Imputation. 24th International Conference on Pattern Recognition (ICPR), Beijing, pp 1450\u20131455","DOI":"10.1109\/ICPR.2018.8546269"},{"key":"1560_CR24","doi-asserted-by":"publisher","first-page":"12983","DOI":"10.1109\/ACCESS.2018.2803755","volume":"6","author":"X Xu","year":"2018","unstructured":"Xu X, Chong W, Li S, Arabo A, Xiao J (2018) MIAEC: Missing Data Imputation Based on the Evidence Chain. IEEE Access 6:12983\u201312992","journal-title":"IEEE Access"},{"issue":"2","key":"1560_CR25","doi-asserted-by":"publisher","first-page":"1610","DOI":"10.1109\/JSYST.2016.2576026","volume":"12","author":"L Zhao","year":"2018","unstructured":"Zhao L, Chen Z, Yang Z, Hu Y, Obaidat MS (2018) Local Similarity Imputation Based on Fast Clustering for Incomplete Data in Cyber-Physical Systems. IEEE Syst J 12(2):1610\u20131620","journal-title":"IEEE Syst J"},{"key":"1560_CR26","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.knosys.2018.03.026","volume":"151","author":"CF Tsai","year":"2018","unstructured":"Tsai CF, Li ML, Lin WC (2018) A class center based approach for missing value imputation. Knowl-Based Syst 151:124\u2013135","journal-title":"Knowl-Based Syst"},{"key":"1560_CR27","unstructured":"Yuan YC (2010) Multiple imputation for missing data: Concepts and new development (Version 9.0), SAS Institute Inc, Rockville, MD, pp 49"},{"key":"1560_CR28","series-title":"C&H\/CRC Monographs on Statistics & Applied Probability","doi-asserted-by":"publisher","DOI":"10.1201\/9781439821862","volume-title":"Analysis of Incomplete Multivariate Data","author":"J Schafer","year":"1997","unstructured":"Schafer JL (1997) Analysis of incomplete multivariate data. CRC Press, Boca Raton"},{"issue":"7553","key":"1560_CR29","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"8","key":"1560_CR30","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"B Yoshua","year":"2013","unstructured":"Yoshua B, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"1560_CR31","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.4249\/scholarpedia.5947","volume":"4","author":"GE Hinton","year":"2009","unstructured":"Hinton GE (2009) Deep belief networks. Scholarpedia 4(6):5947","journal-title":"Scholarpedia"},{"issue":"7","key":"1560_CR32","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"key":"1560_CR33","unstructured":"Krizhevsky A, Hinton GE (2011) Using very deep autoencoders for content-based image retrieval. ESANN 2011 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium), pp 27\u201329"},{"key":"1560_CR34","doi-asserted-by":"crossref","unstructured":"Hajinoroozi M, Jung T, Lin C, Huang Y (2015) Feature extraction with deep belief networks for driver\u2019s cognitive states prediction from EEG data. IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), pp 812\u2013815","DOI":"10.1109\/ChinaSIP.2015.7230517"},{"key":"1560_CR35","doi-asserted-by":"crossref","unstructured":"Chen Z, Liu S, Jiang K, Xu H, Cheng X (2015) A data imputation method based on deep belief network. IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing, Liverpool, pp 1238\u20131243","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.184"},{"issue":"1","key":"1560_CR36","first-page":"113","volume":"59","author":"SB Green","year":"2012","unstructured":"Green SB, Salkind NJ, Akey TM, Hall P (2012) Using SPSS for Windows: analyzing and understanding data. Am Stat 59(1):113\u2013113","journal-title":"Am Stat"},{"issue":"8","key":"1560_CR37","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31(8):651\u2013666","journal-title":"Pattern Recogn Lett"},{"key":"1560_CR38","doi-asserted-by":"crossref","unstructured":"Sacerdoti FD, Katz MJ, Massie ML (2003) Wide area cluster monitoring with Ganglia. In: IEEE International Conference on CLUSTER Computin, pp 289\u2013298","DOI":"10.1109\/CLUSTR.2003.1253327"},{"issue":"3","key":"1560_CR39","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1207\/S15328007SEM0703_1","volume":"7","author":"MS Gold","year":"2000","unstructured":"Gold MS, Bentler PM (2000) Treatments of missing data: A Monte Carlo comparison of RBHDI, iterative stochastic regression imputation, and expectation-maximization. Struct Equ Model 7(3):319\u2013355","journal-title":"Struct Equ Model"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01560-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-019-01560-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01560-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T21:54:36Z","timestamp":1695333276000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-019-01560-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,19]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["1560"],"URL":"https:\/\/doi.org\/10.1007\/s10489-019-01560-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,19]]},"assertion":[{"value":"19 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}