Knowledge Discovery

Knowledge discovery in databases is a broad process of finding useful knowledge from a collection of data. This is a process of discovering and identifying the ultimately important information from the observed results. According to Techopedia 2021, KDD encompasses data storage and access, scaling algorithms to massive data sets and interpreting results. It now houses many different approaches to discovery, which includes inductive learning, Bayesian statistics, semantic query optimization, knowledge acquisition for expert systems and information theory. The ultimate goal is to extract high-level knowledge from low-level data.

KDD includes multidisciplinary activities.

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Data mining integrates with the components of statistics and AL, ML, and Pattern Recognition in a way that data mining uses power of access list, machine learning, statistics and database techniques to mine large databases and come up with patterns. All of them are interesting data driven disciplines that help organizations make better decisions and positively affect the growth of any business.

 

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