Data Mining: Evaluation of Invisible Patterns of Data

Running Head: DATA MINING 1
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DATA MINING 2
Introduction
Data mining is evaluation of invisible patterns of data according to variety of concepts of
grouping into useful information, which is gathered and combined in common areas, such as data
warehouses, for convenient analysis, algorithms of data mining, enhancing making of decisions
in business and other information necessities to ultimately minimize costs and increase revenue.
There is a lot of information found in information industry. The data is valueless if it is not
turned into productive information. It is important to evaluate the large quantity of data and
obtain productive information from it. Apart from being used to extract information, data mining
also involve other activities like data cleaning, translation, integration, evaluation and date
presentation. After carrying out all of the processes, the information can now be used to carry out
practices such as management of production, inspection of fraud, studies of science and
evaluation of market. (Simbarashe, 2011).
Despite the fact that Data mining is usually associated with the new technology. It is
however a subject with a long history. In 1763, Thomas Bayer’s paper was published and it
introduced Baye’s theorem which is significant in probability and data mining. In 1805, Adrien-
Marie Legendre and Carl Friedrich Gauss used knowledge of regression to investigate the orbits
of about the sun. In 1943, Walter Pitts and Wareen McCulloch generated a notional model of a
neuron network which can be used to obtain and process input then produce output.
Fig: Evolution of data mining