Cluster analysis or clustering is a statistical classification technique or activity that involves grouping a set of objects or data in such a way that those contained in the same group (cluster) will be similar to each other but different from those in the other group. It is required for data mining and exploration, and is commonly used in the bioinformatics industry and other sectors within the scope of machine learning, pattern recognition, image analysis, and analyzing large datasets.
Generative Adversarial Networks (GANs) are artificial intelligence models that generate realistic data by training two neural networks (generator and discriminator) in a competing learning mechanism. Many variants of this technology have been developed for different use cases.
Data Observability is the ability to monitor, diagnose, and manage the quality of data throughout the data lifecycle. It is also the discipline to automatically find out the health of your data and solve problems as soon as possible.
Dirty data refers to data that is wrong for a company. This inaccuracy not only means that the data is not correct, the correct data can also be “dirty”.
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