Digital Data Forgetting: A Machine Learning Approach - Aşıroğlu, Batuhan; Yıldız, Eyyüp; Zencirli, Ahmet; Ensari, Tolga; Nalçakan, Yağız; GÜNAY, MELİKE; METE, BÜŞRA RÜMEYSA (My University, 2018)
Digital 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.
Keywords: Digital Data Forgetting; Machine Learning; Deep Autoencoder; Big Cleaning