Knowledge Discovery in Data-Mining

Knowledge discovery in databases (KDD) is discovering useful knowledge from data collection. The data mining process aims to extract information from a data set and transform it into an understandable structure for further use. Data mining is just one step of the knowledge discovery process (the core step). Some following steps are pattern evaluation (this step interprets mined patterns and relationships), akin to your analytic process, and knowledge consolidation, similar to reporting your findings, although they ought to be more robust than simply consolidating knowledge to respond responsibly to your requirements. Like analysis, KDD is an iterative process. If the pattern evaluated after the data mining step is not useful, the process can begin again from the previous steps.

Abstract

Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD) an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

A warehouse is a commercial building for storage of goods. It is manufacturers, importers, exporters, wholesalers, transport businesses, customs, etc. They are usually large plain buildings in industrial areas of cities and towns and villages.

Source: Shivali, Joni Birla and Gurpreet, https://www.ijert.org/knowledge-discovery-in-data-mining
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