Main Article Content
Abstract
From the last 20 years digital transformation throughout the world goes on increasing very fast. Nowadays very important and powerful is data. In earlier days, to collect the data magnetic tapes were used after that digital data storage was used. But now the extremely popular method is Big Data with its tool ML in each industry and literature. In order to obtain a powerful/meaningful/valuable data, people use machine learning. The results which come are very valuable for planning. However, these days it's quite difficultto collect the digital data and utilize it in computers. With this expensive process, in the future,there would be enough storage space to store the data. Therefore, we want and propose the phrase "Digital knowledge forgetting" with the ML approach. Because of this software or the digital solution, we can erase the data which is not required and get some more valuable data. We referred to this process as "Big Cleaning". In this article, the set of datathat we will use is to get and extract the additional valuable and meaningful information with Deep Autoencoder, k-nearest neighbor (k-NN), and Principal Component Analysis (PCA) machine learning algorithms for the experimental and analysis purpose.