**1.1 Data Mining**

Data mining may be thought of as a natural progression of information technology. It can be simply defined as a procedure for searching, gathering, filtering, and analyzing data. It's the method of extracting useful knowledge from vast volumes of data kept in databases, data centers, or other data repositories. Database technology, statistics, artificial intelligence, machine learning, high-performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis are all techniques used in data mining. Data mining allows for the extraction of interesting knowledge, regularities, or highlevel information from databases, which can then be viewed or browsed from various perspectives.

It deals with the secondary study of massive databases in order to uncover previously unknown relationships that are of interest or benefit to database owners. It can be thought of as computer-assisted exploratory data analysis of massive, complex data sets from a statistical standpoint. Data mining is having a big effect in business, industry, and science right now. It also opens up a lot of possibilities for new methodological advances in science. New issues occur, partly as a result of the sheer scale of the data sets in question, and partly as a result of pattern matching issues.

Decision making, process control, information management, and query processing are only a few of the applications for the newly discovered experience. As a result, data mining is recognized as one of the most exciting modern database technologies in the information industry, as well as one of the most important frontiers in database systems [1, 2]. This chapter will go into the basics of data mining as well as the data extraction techniques. Mastering this technology and its techniques will have significant advantages as well as a competitive edge.
