Knowledge Discovery in Databases: An Overview

Knowledge Discovery in Databases: Overview Previously, the term data mining was used to specify activities for extracting useful information from a database. Currently, this term is thought to apply to activities in very large processes for extracting knowledge from opaque databases. This whole process is called Knowledge Discovery (KDD) in the database. This process consists of many subprocesses, linking them provides a solid foundation for acquiring knowledge from large databases.

Knowledge discovery in databases Data mining information analysis is related to knowledge discovery (KDD) and data mining (DM) which are currently known as databases. A general premise is to be stored in the repository (from an economic point of view, science or technology perspective) to extract and process the information to make the data "useful". The value of "original" information comes from information used to process and generate "developed", ie higher levels, which may be useful for decisions in specific activity areas. . 219 - 245))

Knowledge extraction is mainly related to the discovery process called Knowledge Discovery (KDD) in the database. This refers to an important process of discovering knowledge and potentially useful information in the data contained in some information bases. This is not an automated process, it's an iterative process that can examine a very large amount of data in detail to determine relationships. This is the process of extracting quality information that can be used to draw conclusions based on relationships or models in the data.

1996 Usama Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth announced "Discovery of knowledge from data mining to database". Data mining, knowledge extraction, information discovery, information gathering, data archeology, data pattern processing etc names. KDD refers to the entire process of discovering useful knowledge from data. Data mining is to apply a specific algorithm to extract patterns from data. Other steps of the KDD process such as data preparation, data selection, data cleansing, integration of appropriate prior knowledge. In order to ensure useful knowledge from the data, it is essential to correctly interpret the mining results.