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dc.contributor.authorMete, Büşra Rümeysa
dc.contributor.authorGünay, Melike
dc.contributor.authorAşıroğlu, Batuhan
dc.contributor.authorYıldız, Eyyüp
dc.contributor.authorZencirli, Ahmet
dc.contributor.authorEnsari, Tolga
dc.contributor.authorNalçakan, Yağız
dc.date.accessioned2019-08-23T09:24:03Z
dc.date.available2019-08-23T09:24:03Z
dc.date.issued2018-10
dc.identifier.urihttps://hdl.handle.net/11413/5123
dc.description.abstractDigital transformation of the world goes very fast during last two decades. Today, data is power and very important. Firstly, magnetic tapes and then digital data storages have been used to collect all data. After this process, big data and its tool machine learning became very popular in both literature and industry. People use machine learning in order to obtain meaningful information from the big data. It brings valuable planning results. However, nowadays it is quite hard to collect and store all digital data to computers. This process is expensive and we will not have enough space to store data in the future. Therefore, we need and propose "Digital Data Forgetting" phrase with machine learning approach. With this digital / software solution, we will have more valuable data and will be able to erase the rest of the them. We called this operation "Big Cleaning". In this article, we use data set to get and extract more valuable data with principal component analysis (PCA), deep autoencoder and k-nearest neighbor machine learning methods in the experimental analysis section.tr_TR
dc.language.isoen_UStr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectDigital Data Forgettingtr_TR
dc.subjectMachine Learningtr_TR
dc.subjectDeep Autoencodertr_TR
dc.subjectBig Cleaningtr_TR
dc.titleDigital Data Forgetting: A Machine Learning Approachtr_TR
dc.typeconferenceObjecttr_TR
dc.contributor.authorID283334tr_TR
dc.contributor.authorID286270tr_TR
dc.contributor.authorID261185tr_TR
dc.contributor.authorID176400tr_TR
dc.contributor.authorID270484tr_TR
dc.relation.journalISMSIT 2018- International Symposium on Multidisciplinary Studies and Innovative Technolojiestr_TR
dc.identifier.wos000467794200093
dc.identifier.wos467794200093en


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States